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1 | 1/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/1/aim_paper/aim_paper.txt | {
"id": "435f070f4779a184285917184899fbdb43bb6d7e",
"title": "MAGE: Machine-generated Text Detection in the Wild",
"abstract": "Large language models (LLMs) have achieved human-level text generation, emphasizing the need for effective AI-generated text detection to mitigate risks like the spread of fake new... | 685c152b5cd7146703b68561919fbd3e058961fe60c7a0f7f1351c7cd4ee801f | 435f070f4779a184285917184899fbdb43bb6d7e | MAGE: Machine-generated Text Detection in the Wild | Large language models (LLMs) have achieved human-level text generation, emphasizing the need for effective AI-generated text detection to mitigate risks like the spread of fake news and plagiarism. Existing research has been constrained by evaluating detection methods on specific domains or particular language models. ... | 48 | 8 | 15 | 15 |
2 | 2/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/2/aim_paper/aim_paper.txt | {
"id": "e8e6d462d0a7656a9fefec21075159fd2138a780",
"title": "GenTranslate: Large Language Models are Generative Multilingual Speech and Machine Translators",
"abstract": "Recent advances in large language models (LLMs) have stepped forward the development of multilingual speech and machine translation by i... | d7344767762db5fabddc6614b2773e1e8c6e551cb830c0a000474c6dd549895d | e8e6d462d0a7656a9fefec21075159fd2138a780 | GenTranslate: Large Language Models are Generative Multilingual Speech and Machine Translators | Recent advances in large language models (LLMs) have stepped forward the development of multilingual speech and machine translation by its reduced representation errors and incorporated external knowledge. However, both translation tasks typically utilize beam search decoding and top-1 hypothesis selection for inferenc... | 46 | 7 | 15 | 15 |
3 | 3/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/3/aim_paper/aim_paper.txt | {
"id": "52258e9110f8e73cca24f6394bfc483341b459a4",
"title": "BitDistiller: Unleashing the Potential of Sub-4-Bit LLMs via Self-Distillation",
"abstract": "The upscaling of Large Language Models (LLMs) has yielded impressive advances in natural language processing, yet it also poses significant deployment c... | 52f34219e2174d9c0b930b55ef8bc7cf13e7e82e33ae58750d639bd817fb48d3 | 52258e9110f8e73cca24f6394bfc483341b459a4 | BitDistiller: Unleashing the Potential of Sub-4-Bit LLMs via Self-Distillation | The upscaling of Large Language Models (LLMs) has yielded impressive advances in natural language processing, yet it also poses significant deployment challenges. Weight quantization has emerged as a widely embraced solution to reduce memory and computational demands. This paper introduces BitDistiller, a framework tha... | 33 | 7 | 15 | 15 |
4 | 4/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/4/aim_paper/aim_paper.txt | {
"id": "5088a04d1a9f42b967f3dcf791145e8aa367fc54",
"title": "How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition",
"abstract": "Large language models (LLMs) with enormous pre-training tokens and parameters emerge diverse abilities, including math reasoning, code g... | 9ed244f5d160c92376a5efa6d01887a8080f6b08b576d1d4dabbc1f87021cb9e | 5088a04d1a9f42b967f3dcf791145e8aa367fc54 | How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition | Large language models (LLMs) with enormous pre-training tokens and parameters emerge diverse abilities, including math reasoning, code generation, and instruction following. These abilities are further enhanced by supervised fine-tuning (SFT). While the open-source community has explored ad-hoc SFT for enhancing indivi... | 51 | 5 | 15 | 15 |
5 | 5/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/5/aim_paper/aim_paper.txt | {
"id": "002bf0720404e5dc6bf43eff64f116ec755b405f",
"title": "SciMON: Scientific Inspiration Machines Optimized for Novelty",
"abstract": "We explore and enhance the ability of neural language models to generate novel scientific directions grounded in literature. Work on literature-based hypothesis generati... | b917af2eabff1ee963060e598a6081fcf86127054e0187238b971b0571829abe | 002bf0720404e5dc6bf43eff64f116ec755b405f | SciMON: Scientific Inspiration Machines Optimized for Novelty | We explore and enhance the ability of neural language models to generate novel scientific directions grounded in literature. Work on literature-based hypothesis generation has traditionally focused on binary link prediction--severely limiting the expressivity of hypotheses. This line of work also does not focus on opti... | 32 | 4 | 15 | 15 |
6 | 6/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/6/aim_paper/aim_paper.txt | {
"id": "e0702a22e0841c54ab865b4996d7b07af192a3e1",
"title": "Mementos: A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image Sequences",
"abstract": "Multimodal Large Language Models (MLLMs) have demonstrated proficiency in handling a variety of visual-language tasks. However, ... | 7aeb936270a867f1d547a20d2f32b612c9035d4f86a074643f8ef77415db11f4 | e0702a22e0841c54ab865b4996d7b07af192a3e1 | Mementos: A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image Sequences | Multimodal Large Language Models (MLLMs) have demonstrated proficiency in handling a variety of visual-language tasks. However, current MLLM benchmarks are predominantly designed to evaluate reasoning based on static information about a single image, and the ability of modern MLLMs to extrapolate from image sequences, ... | 27 | 11 | 3 | 15 |
7 | 7/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/7/aim_paper/aim_paper.txt | {
"id": "01efb3fd2d3ae4b5f4389c916c94f2c6d9c11b81",
"title": "Explore Spurious Correlations at the Concept Level in Language Models for Text Classification",
"abstract": "Language models (LMs) have achieved notable success in numerous NLP tasks, employing both fine-tuning and in-context learning (ICL) metho... | 1838f7ec63e3e6361d82e24de94342c4387c291400439f5b4772da7a69558cfc | 01efb3fd2d3ae4b5f4389c916c94f2c6d9c11b81 | Explore Spurious Correlations at the Concept Level in Language Models for Text Classification | Language models (LMs) have achieved notable success in numerous NLP tasks, employing both fine-tuning and in-context learning (ICL) methods. While language models demonstrate exceptional performance, they face robustness challenges due to spurious correlations arising from imbalanced label distributions in training dat... | 51 | 5 | 15 | 15 |
8 | 8/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/8/aim_paper/aim_paper.txt | {
"id": "5ec2aa6c84e4b7ee51c6cc3e4cd74ed3b21c2df1",
"title": "FOFO: A Benchmark to Evaluate LLMs' Format-Following Capability",
"abstract": "This paper presents FoFo, a pioneering benchmark for evaluating large language models' (LLMs) ability to follow complex, domain-specific formats, a crucial yet underex... | 674201a5c7e17d9b49feaa23dc5dca51858814bf0c91e1ddb1358f491f92efc5 | 5ec2aa6c84e4b7ee51c6cc3e4cd74ed3b21c2df1 | FOFO: A Benchmark to Evaluate LLMs' Format-Following Capability | This paper presents FoFo, a pioneering benchmark for evaluating large language models' (LLMs) ability to follow complex, domain-specific formats, a crucial yet underexamined capability for their application as AI agents. Despite LLMs' advancements, existing benchmarks fail to assess their format-following proficiency a... | 22 | 7 | 15 | 15 |
9 | 9/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/9/aim_paper/aim_paper.txt | {
"id": "fe6670cfc0d0dfe184afc8e003df51333d3a750e",
"title": "The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants",
"abstract": "We present Belebele, a multiple-choice machine reading comprehension (MRC) dataset spanning 122 language variants. Significantly expanding th... | cad0a7c2cb775235242cea102f4fb9e5bd94220a72fdc2f25ba45fb202e79031 | fe6670cfc0d0dfe184afc8e003df51333d3a750e | The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants | We present Belebele, a multiple-choice machine reading comprehension (MRC) dataset spanning 122 language variants. Significantly expanding the language coverage of natural language understanding (NLU) benchmarks, this dataset enables the evaluation of text models in high-, medium-, and low-resource languages. Each ques... | 67 | 9 | 15 | 15 |
10 | 10/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/10/aim_paper/aim_paper.txt | {
"id": "3dd564a7500861162904c6875a102118891a03f9",
"title": "AbsInstruct: Eliciting Abstraction Ability from LLMs through Explanation Tuning with Plausibility Estimation",
"abstract": "Abstraction ability is crucial in human intelligence, which can also benefit various tasks in NLP study. Existing work sho... | e813989df72aa08f5dbedc311775f534de308ef23d094bb0fcee3511e244c598 | 3dd564a7500861162904c6875a102118891a03f9 | AbsInstruct: Eliciting Abstraction Ability from LLMs through Explanation Tuning with Plausibility Estimation | Abstraction ability is crucial in human intelligence, which can also benefit various tasks in NLP study. Existing work shows that LLMs are deficient in abstract ability, and how to improve it remains unexplored. In this work, we design the framework AbsInstruct to enhance LLMs' abstraction ability through instruction t... | 56 | 10 | 15 | 15 |
11 | 11/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/11/aim_paper/aim_paper.txt | {
"id": "c4d43fe1b7e44c5e9929d6edf7bd11de4e6d293a",
"title": "Self-Distillation Bridges Distribution Gap in Language Model Fine-Tuning",
"abstract": "The surge in Large Language Models (LLMs) has revolutionized natural language processing, but fine-tuning them for specific tasks often encounters challenges ... | c292b5d32d07219656efe4b724ea71c7dc21761186bdabd394ccb61111c620dc | c4d43fe1b7e44c5e9929d6edf7bd11de4e6d293a | Self-Distillation Bridges Distribution Gap in Language Model Fine-Tuning | The surge in Large Language Models (LLMs) has revolutionized natural language processing, but fine-tuning them for specific tasks often encounters challenges in balancing performance and preserving general instruction-following abilities. In this paper, we posit that the distribution gap between task datasets and the L... | 35 | 6 | 15 | 15 |
12 | 12/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/12/aim_paper/aim_paper.txt | {
"id": "f131b342e3aede46d24afc9b9055a94cceb0936a",
"title": "InstructProtein: Aligning Human and Protein Language via Knowledge Instruction",
"abstract": "Large Language Models (LLMs) have revolutionized the field of natural language processing, but they fall short in comprehending biological sequences suc... | 5a61be3495fb946fee785c728dea5524ca71ec7dffb94003acc222bcf2b36782 | f131b342e3aede46d24afc9b9055a94cceb0936a | InstructProtein: Aligning Human and Protein Language via Knowledge Instruction | Large Language Models (LLMs) have revolutionized the field of natural language processing, but they fall short in comprehending biological sequences such as proteins. To address this challenge, we propose InstructProtein, an innovative LLM that possesses bidirectional generation capabilities in both human and protein l... | 57 | 7 | 15 | 15 |
13 | 13/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/13/aim_paper/aim_paper.txt | {
"id": "bad287184c6739fd6f476f89cb83e09415982d9f",
"title": "ANALOGYKB: Unlocking Analogical Reasoning of Language Models with A Million-scale Knowledge Base",
"abstract": "Analogical reasoning is a fundamental cognitive ability of humans. However, current language models (LMs) still struggle to achieve hu... | ac85fdf77269bf33ad059160e6fe1c0db224b7feb9948ce5cd635c6a00ec1017 | bad287184c6739fd6f476f89cb83e09415982d9f | ANALOGYKB: Unlocking Analogical Reasoning of Language Models with A Million-scale Knowledge Base | Analogical reasoning is a fundamental cognitive ability of humans. However, current language models (LMs) still struggle to achieve human-like performance in analogical reasoning tasks due to a lack of resources for model training. In this work, we address this gap by proposing ANALOGYKB, a million-scale analogy knowle... | 49 | 6 | 15 | 15 |
14 | 14/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/14/aim_paper/aim_paper.txt | {
"id": "16d6e1ed1cf72212f6154644f3aa59d18bc95fda",
"title": "DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models",
"abstract": "In the era of large language models, Mixture-of-Experts (MoE) is a promising architecture for managing computational costs when scaling up mo... | a5abdf7596ff2985715fc402ca666e94d971c484d907fea0308205ba766877c4 | 16d6e1ed1cf72212f6154644f3aa59d18bc95fda | DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models | In the era of large language models, Mixture-of-Experts (MoE) is a promising architecture for managing computational costs when scaling up model parameters. However, conventional MoE architectures like GShard, which activate the top-$K$ out of $N$ experts, face challenges in ensuring expert specialization, i.e. each ex... | 54 | 16 | 15 | 15 |
15 | 15/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/15/aim_paper/aim_paper.txt | {
"id": "015f62d7a59f7a4301c0cdbe997460c38148d07b",
"title": "Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized Rehearsal",
"abstract": "Large language models (LLMs) suffer from catastrophic forgetting during continual learning. Conventional rehearsal-based methods rely on pr... | 33ab3c9990b84125b1eae95b2523ae232c0eecf50097b047541678ea153a16a9 | 015f62d7a59f7a4301c0cdbe997460c38148d07b | Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized Rehearsal | Large language models (LLMs) suffer from catastrophic forgetting during continual learning. Conventional rehearsal-based methods rely on previous training data to retain the model's ability, which may not be feasible in real-world applications. When conducting continual learning based on a publicly-released LLM checkpo... | 23 | 8 | 15 | 15 |
16 | 16/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/16/aim_paper/aim_paper.txt | {
"id": "0d22f06a1f5ad9f62b2f35c126b514f927586c85",
"title": "Enhancing Large Language Models in Coding Through Multi-Perspective Self-Consistency",
"abstract": "Large language models (LLMs) have exhibited remarkable ability in code generation. However, generating the correct solution in a single attempt st... | 7a6e019624b58eacf77625a993ff97d708acd8b3287c0d422939b1d2aa315650 | 0d22f06a1f5ad9f62b2f35c126b514f927586c85 | Enhancing Large Language Models in Coding Through Multi-Perspective Self-Consistency | Large language models (LLMs) have exhibited remarkable ability in code generation. However, generating the correct solution in a single attempt still remains a challenge. Prior works utilize verification properties in software engineering to verify and re-rank solutions in a majority voting manner. But the assumption b... | 37 | 4 | 15 | 15 |
17 | 17/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/17/aim_paper/aim_paper.txt | {
"id": "31e27369f64c51ffbfd9bc8e3cbb20705edc6bff",
"title": "Citation-Enhanced Generation for LLM-based Chatbots",
"abstract": "Large language models (LLMs) exhibit powerful general intelligence across diverse scenarios, including their integration into chatbots. However, a vital challenge of LLM-based cha... | 85649601867af2c8b4769d57d09755efd62854d8c7bfb3ba768b28a2c26a81c0 | 31e27369f64c51ffbfd9bc8e3cbb20705edc6bff | Citation-Enhanced Generation for LLM-based Chatbots | Large language models (LLMs) exhibit powerful general intelligence across diverse scenarios, including their integration into chatbots. However, a vital challenge of LLM-based chatbots is that they may produce hallucinated content in responses, which significantly limits their applicability. Various efforts have been m... | 23 | 4 | 15 | 15 |
18 | 18/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/18/aim_paper/aim_paper.txt | {
"id": "4c0428917aeee6aa7bd434f337d039f35996b736",
"title": "LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression",
"abstract": "In long context scenarios, large language models (LLMs) face three main challenges: higher computational cost, performance reduction, a... | e550a474932ece6bfd43d309d063833dcd2b8d83675de0366569f9b5c4afaca2 | 4c0428917aeee6aa7bd434f337d039f35996b736 | LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression | In long context scenarios, large language models (LLMs) face three main challenges: higher computational cost, performance reduction, and position bias. Research indicates that LLM performance hinges on the density and position of key information in the input prompt. Inspired by these findings, we propose LongLLMLingua... | 38 | 7 | 15 | 15 |
19 | 19/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/19/aim_paper/aim_paper.txt | {
"id": "8444b6982b5fa799e016e20f833594bb2f9ab12e",
"title": "UniCoder: Scaling Code Large Language Model via Universal Code",
"abstract": "Intermediate reasoning or acting steps have successfully improved large language models (LLMs) for handling various downstream natural language processing (NLP) tasks. ... | 940703f84c548c802e0a9d8cbbfb5fd1331bfe449af064ca97e4b3227720dbd7 | 8444b6982b5fa799e016e20f833594bb2f9ab12e | UniCoder: Scaling Code Large Language Model via Universal Code | Intermediate reasoning or acting steps have successfully improved large language models (LLMs) for handling various downstream natural language processing (NLP) tasks. When applying LLMs for code generation, recent works mainly focus on directing the models to articulate intermediate natural-language reasoning steps, a... | 42 | 9 | 11 | 15 |
20 | 20/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/20/aim_paper/aim_paper.txt | {
"id": "d4e4685d1f4b490e0e35479f6350a9743673541e",
"title": "LoRAMoE: Alleviating World Knowledge Forgetting in Large Language Models via MoE-Style Plugin",
"abstract": "Supervised fine-tuning (SFT) is a crucial step for large language models (LLMs), enabling them to align with human instructions and enhan... | 715dadf894a102f9c090c856b6d2d21440f0390254408b186e4c41a07869101f | d4e4685d1f4b490e0e35479f6350a9743673541e | LoRAMoE: Alleviating World Knowledge Forgetting in Large Language Models via MoE-Style Plugin | Supervised fine-tuning (SFT) is a crucial step for large language models (LLMs), enabling them to align with human instructions and enhance their capabilities in downstream tasks. Substantially increasing instruction data is a direct solution to align the model with a broader range of downstream tasks or notably improv... | 41 | 16 | 0 | 0 |
21 | 21/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/21/aim_paper/aim_paper.txt | {
"id": "32c5b515cab893e5e4bf3f90c8b6c8262bd7ac09",
"title": "Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation",
"abstract": "Despite showing increasingly human-like abilities, large language models (LLMs) often struggle with factual inaccuracies, i.e.\"hallucinations\", ... | a5e8091be76580e2cf1f26ea4fbf907e25226dcddeed57770097a8ef7238c2f9 | 32c5b515cab893e5e4bf3f90c8b6c8262bd7ac09 | Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation | Despite showing increasingly human-like abilities, large language models (LLMs) often struggle with factual inaccuracies, i.e."hallucinations", even when they hold relevant knowledge. To address these hallucinations, current approaches typically necessitate high-quality human factuality annotations. In this work, we ex... | 40 | 7 | 15 | 15 |
22 | 22/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/22/aim_paper/aim_paper.txt | {
"id": "e4fd996d7caf2f1bef540172cc27b170967828c0",
"title": "Navigating the Metrics Maze: Reconciling Score Magnitudes and Accuracies",
"abstract": "Ten years ago a single metric, BLEU, governed progress in machine translation research. For better or worse, there is no such consensus today, and consequentl... | 7881a8538cb70568ecc9183e873d37bee6cfe02fe64a90bd761d15abd761441a | e4fd996d7caf2f1bef540172cc27b170967828c0 | Navigating the Metrics Maze: Reconciling Score Magnitudes and Accuracies | Ten years ago a single metric, BLEU, governed progress in machine translation research. For better or worse, there is no such consensus today, and consequently it is difficult for researchers to develop and retain the kinds of heuristic intuitions about metric deltas that drove earlier research and deployment decisions... | 33 | 4 | 15 | 15 |
23 | 23/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/23/aim_paper/aim_paper.txt | {
"id": "20f3abdd3640718d8f268aaea8b2ac3b8978d2af",
"title": "Exploring the Potential of Large Language Models in Computational Argumentation",
"abstract": "Computational argumentation has become an essential tool in various domains, including law, public policy, and artificial intelligence. It is an emergi... | 831aafe5a07494bb6dfe301ada5611ed7d126711532f494520fae6f4a7d5fad3 | 20f3abdd3640718d8f268aaea8b2ac3b8978d2af | Exploring the Potential of Large Language Models in Computational Argumentation | Computational argumentation has become an essential tool in various domains, including law, public policy, and artificial intelligence. It is an emerging research field in natural language processing that attracts increasing attention. Research on computational argumentation mainly involves two types of tasks: argument... | 70 | 4 | 15 | 15 |
24 | 24/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/24/aim_paper/aim_paper.txt | {
"id": "5aec6865043cb7c7f281699ae95652e0ff680f09",
"title": "CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning",
"abstract": "The sequential process of conceptualization and instantiation is essential to generalizable commonsense reasoni... | 8142898af15e8e251f57b5a49325b25fa68ac7dc6fa52929e1ebd3a9761b6800 | 5aec6865043cb7c7f281699ae95652e0ff680f09 | CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning | The sequential process of conceptualization and instantiation is essential to generalizable commonsense reasoning as it allows the application of existing knowledge to unfamiliar scenarios. However, existing works tend to undervalue the step of instantiation and heavily rely on pre-built concept taxonomies and human an... | 61 | 12 | 0 | 0 |
25 | 25/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/25/aim_paper/aim_paper.txt | {
"id": "3f081ee658a08d0b3ccc6358c85e618d05f74eb3",
"title": "Time is Encoded in the Weights of Finetuned Language Models",
"abstract": "We present time vectors, a simple tool to customize language models to new time periods. Time vectors are created by finetuning a language model on data from a single time... | ff7760a07fb33bafe00484ac804c66f7f1d1bd8d669b86bcc7720fb570f3de5a | 3f081ee658a08d0b3ccc6358c85e618d05f74eb3 | Time is Encoded in the Weights of Finetuned Language Models | We present time vectors, a simple tool to customize language models to new time periods. Time vectors are created by finetuning a language model on data from a single time (e.g., a year or month), and then subtracting the weights of the original pretrained model. This vector specifies a direction in weight space that, ... | 25 | 3 | 15 | 15 |
26 | 26/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/26/aim_paper/aim_paper.txt | {
"id": "2330035c7586a0dc0b1f09e9c00106b295acf543",
"title": "Long-Context Language Modeling with Parallel Context Encoding",
"abstract": "Extending large language models (LLMs) to process longer inputs is crucial for a wide range of applications. However, the substantial computational cost of transformers ... | 1ea883254d2c0dc2742ce0b7f1fd0f217f3dfe4f93b3fb17905db93ca4f82323 | 2330035c7586a0dc0b1f09e9c00106b295acf543 | Long-Context Language Modeling with Parallel Context Encoding | Extending large language models (LLMs) to process longer inputs is crucial for a wide range of applications. However, the substantial computational cost of transformers and limited generalization of positional encoding restrict the size of their context window. We introduce Context Expansion with Parallel Encoding (CEP... | 61 | 3 | 15 | 15 |
27 | 27/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/27/aim_paper/aim_paper.txt | {
"id": "5a8a6b61033ba2355f7c149cec596c88a1d61954",
"title": "Stumbling Blocks: Stress Testing the Robustness of Machine-Generated Text Detectors Under Attacks",
"abstract": "The widespread use of large language models (LLMs) is increasing the demand for methods that detect machine-generated text to prevent... | 2a5dfcb1bb738fa9468c325c39f5a53aaecbc0fcdf55245ebb96c2dc3edabf0f | 5a8a6b61033ba2355f7c149cec596c88a1d61954 | Stumbling Blocks: Stress Testing the Robustness of Machine-Generated Text Detectors Under Attacks | The widespread use of large language models (LLMs) is increasing the demand for methods that detect machine-generated text to prevent misuse. The goal of our study is to stress test the detectors' robustness to malicious attacks under realistic scenarios. We comprehensively study the robustness of popular machine-gener... | 58 | 7 | 0 | 0 |
28 | 28/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/28/aim_paper/aim_paper.txt | {
"id": "bed35010543191bf57a09a6058e75332702d7afa",
"title": "GSM-Plus: A Comprehensive Benchmark for Evaluating the Robustness of LLMs as Mathematical Problem Solvers",
"abstract": "Large language models (LLMs) have achieved impressive performance across various mathematical reasoning benchmarks. However, ... | 2b560fd5d27f934accd4b978a54fe5734e7433b55d88d19b0b30fb564f96a120 | bed35010543191bf57a09a6058e75332702d7afa | GSM-Plus: A Comprehensive Benchmark for Evaluating the Robustness of LLMs as Mathematical Problem Solvers | Large language models (LLMs) have achieved impressive performance across various mathematical reasoning benchmarks. However, there are increasing debates regarding whether these models truly understand and apply mathematical knowledge or merely rely on shortcuts for mathematical reasoning. One essential and frequently ... | 35 | 5 | 15 | 15 |
29 | 29/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/29/aim_paper/aim_paper.txt | {
"id": "f5b077e01f6e3d91f58cb4ed7158fa61eec5a1f8",
"title": "AutoAct: Automatic Agent Learning from Scratch for QA via Self-Planning",
"abstract": "Language agents have achieved considerable performance on various complex question-answering tasks by planning with external tools. Despite the incessant explo... | 746dc90741100a749b0955d047de2e087cefd49e3cad030a1d70e36553cc322f | f5b077e01f6e3d91f58cb4ed7158fa61eec5a1f8 | AutoAct: Automatic Agent Learning from Scratch for QA via Self-Planning | Language agents have achieved considerable performance on various complex question-answering tasks by planning with external tools. Despite the incessant exploration in this field, existing language agent systems still struggle with costly, non-reproducible data reliance and face the challenge of compelling a single mo... | 43 | 5 | 15 | 15 |
30 | 30/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/30/aim_paper/aim_paper.txt | {
"id": "094b6847434be00e41686529f45d895bd632f68a",
"title": "Can LLMs Learn from Previous Mistakes? Investigating LLMs' Errors to Boost for Reasoning",
"abstract": "Recent works have shown the benefits to LLMs from fine-tuning golden-standard Chain-of-Thought (CoT) rationales or using them as correct examp... | f0742d176359df290af2fc64e653901af83d8b9f43fb16c685ddf66dc9223878 | 094b6847434be00e41686529f45d895bd632f68a | Can LLMs Learn from Previous Mistakes? Investigating LLMs' Errors to Boost for Reasoning | Recent works have shown the benefits to LLMs from fine-tuning golden-standard Chain-of-Thought (CoT) rationales or using them as correct examples in few-shot prompting. While humans can indeed imitate correct examples, learning from our mistakes is another vital aspect of human cognition. Hence, a question naturally ar... | 37 | 2 | 15 | 15 |
31 | 31/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/31/aim_paper/aim_paper.txt | {
"id": "33c8910107f3fcb17d140cc88554652508ae3674",
"title": "Detoxifying Large Language Models via Knowledge Editing",
"abstract": "This paper investigates using knowledge editing techniques to detoxify Large Language Models (LLMs). We construct a benchmark, SafeEdit, which covers nine unsafe categories wi... | 3a2d13910258efef8a6def199dcd3b4a2b197816c7aebd9fb75a48341f2fc451 | 33c8910107f3fcb17d140cc88554652508ae3674 | Detoxifying Large Language Models via Knowledge Editing | This paper investigates using knowledge editing techniques to detoxify Large Language Models (LLMs). We construct a benchmark, SafeEdit, which covers nine unsafe categories with various powerful attack prompts and equips comprehensive metrics for systematic evaluation. We conduct experiments with several knowledge edit... | 57 | 7 | 0 | 0 |
32 | 32/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/32/aim_paper/aim_paper.txt | {
"id": "e327ef8d46ea0413316c80ee1404453834d84f05",
"title": "Black-Box Prompt Optimization: Aligning Large Language Models without Model Training",
"abstract": "Large language models (LLMs) have shown impressive success in various applications. However, these models are often not well aligned with human in... | 704047e7526a69583e22ad0b2d6f3463c1b8767077f473ee18065de1d0195e98 | e327ef8d46ea0413316c80ee1404453834d84f05 | Black-Box Prompt Optimization: Aligning Large Language Models without Model Training | Large language models (LLMs) have shown impressive success in various applications. However, these models are often not well aligned with human intents, which calls for additional treatments on them; that is, the alignment problem. To make LLMs better follow user instructions, existing alignment methods primarily focus... | 33 | 7 | 0 | 0 |
33 | 33/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/33/aim_paper/aim_paper.txt | {
"id": "19e909f88b8b9b0635bd6e441094e1738c3bba9a",
"title": "Unified Hallucination Detection for Multimodal Large Language Models",
"abstract": "Despite significant strides in multimodal tasks, Multimodal Large Language Models (MLLMs) are plagued by the critical issue of hallucination. The reliable detecti... | 187ebacf62d170e377802061e5ade3bca9a81cea684c738f436966f52c289cd0 | 19e909f88b8b9b0635bd6e441094e1738c3bba9a | Unified Hallucination Detection for Multimodal Large Language Models | Despite significant strides in multimodal tasks, Multimodal Large Language Models (MLLMs) are plagued by the critical issue of hallucination. The reliable detection of such hallucinations in MLLMs has, therefore, become a vital aspect of model evaluation and the safeguarding of practical application deployment. Prior r... | 39 | 6 | 15 | 15 |
34 | 34/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/34/aim_paper/aim_paper.txt | {
"id": "d6354e91d8dcf73bff50097b76a81de874f7bd7a",
"title": "OceanGPT: A Large Language Model for Ocean Science Tasks",
"abstract": "Ocean science, which delves into the oceans that are reservoirs of life and biodiversity, is of great significance given that oceans cover over 70% of our planet's surface. R... | abfe6bdb1c881d256b794e5033c0ee18aa0a728739e327109d09b978e3abf07a | d6354e91d8dcf73bff50097b76a81de874f7bd7a | OceanGPT: A Large Language Model for Ocean Science Tasks | Ocean science, which delves into the oceans that are reservoirs of life and biodiversity, is of great significance given that oceans cover over 70% of our planet's surface. Recently, advances in Large Language Models (LLMs) have transformed the paradigm in science. Despite the success in other domains, current LLMs oft... | 34 | 2 | 15 | 15 |
35 | 35/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/35/aim_paper/aim_paper.txt | {
"id": "e534e65562e945cc67f4075ac2757051fc188ea8",
"title": "Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts",
"abstract": "Large language models (LLMs) are known to effectively perform tasks by simply observing few exemplars.... | a380ada429fdc029de9c722533a7998e257c0597a68196591009de72705efb90 | e534e65562e945cc67f4075ac2757051fc188ea8 | Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts | Large language models (LLMs) are known to effectively perform tasks by simply observing few exemplars. However, in low-resource languages, obtaining such hand-picked exemplars can still be challenging, where unsupervised techniques may be necessary. Moreover, competent generative capabilities of LLMs are observed only ... | 43 | 4 | 15 | 15 |
36 | 36/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/36/aim_paper/aim_paper.txt | {
"id": "4bebe389dfa85423e5cc089edf20b2c3f572f38c",
"title": "Self-Contrast: Better Reflection Through Inconsistent Solving Perspectives",
"abstract": "The reflection capacity of Large Language Model (LLM) has garnered extensive attention. A post-hoc prompting strategy, e.g., reflexion and self-refine, refi... | ee2a51fff49643c16f82b8ba4b1b8e497058c914d20916dc01f7e96b3dce7d2a | 4bebe389dfa85423e5cc089edf20b2c3f572f38c | Self-Contrast: Better Reflection Through Inconsistent Solving Perspectives | The reflection capacity of Large Language Model (LLM) has garnered extensive attention. A post-hoc prompting strategy, e.g., reflexion and self-refine, refines LLM's response based on self-evaluated or external feedback. However, recent research indicates without external feedback, LLM's intrinsic reflection is unstabl... | 60 | 6 | 15 | 15 |
37 | 37/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/37/aim_paper/aim_paper.txt | {
"id": "9a8d0f0ace05d3795a9a58f94675710b89006941",
"title": "Relying on the Unreliable: The Impact of Language Models' Reluctance to Express Uncertainty",
"abstract": "As natural language becomes the default interface for human-AI interaction, there is a need for LMs to appropriately communicate uncertaint... | 7aaaec8e18a28bd4703651c9a0389a38b9b93f59351e3621dfb263f5f40f369d | 9a8d0f0ace05d3795a9a58f94675710b89006941 | Relying on the Unreliable: The Impact of Language Models' Reluctance to Express Uncertainty | As natural language becomes the default interface for human-AI interaction, there is a need for LMs to appropriately communicate uncertainties in downstream applications. In this work, we investigate how LMs incorporate confidence in responses via natural language and how downstream users behave in response to LM-artic... | 51 | 3 | 15 | 15 |
38 | 38/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/38/aim_paper/aim_paper.txt | {
"id": "bcf2c7e3f4ed64c8294c35a59220a26dd4f40060",
"title": "OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems",
"abstract": "Recent advancements have seen Large Language Models (LLMs) and Large Multimodal Models (LMMs) surpassing general ... | ddb4845bffd197460d4c7669795f715d3b7d6fb1082e3f3870e0defa4352fc4b | bcf2c7e3f4ed64c8294c35a59220a26dd4f40060 | OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems | Recent advancements have seen Large Language Models (LLMs) and Large Multimodal Models (LMMs) surpassing general human capabilities in various tasks, approaching the proficiency level of human experts across multiple domains. With traditional benchmarks becoming less challenging for these models, new rigorous challenge... | 34 | 13 | 0 | 0 |
39 | 39/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/39/aim_paper/aim_paper.txt | {
"id": "8353270c28a542735c1ce8af2ff998146b620844",
"title": "Exploring Memorization in Fine-tuned Language Models",
"abstract": "Large language models (LLMs) have shown great capabilities in various tasks but also exhibited memorization of training data, raising tremendous privacy and copyright concerns. W... | 85efc6f795c563e88180297c810c6b53cd9c7471ecdd2b038f43476cd7257d3f | 8353270c28a542735c1ce8af2ff998146b620844 | Exploring Memorization in Fine-tuned Language Models | Large language models (LLMs) have shown great capabilities in various tasks but also exhibited memorization of training data, raising tremendous privacy and copyright concerns. While prior works have studied memorization during pre-training, the exploration of memorization during fine-tuning is rather limited. Compared... | 29 | 10 | 0 | 0 |
40 | 40/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/40/aim_paper/aim_paper.txt | {
"id": "c59628de894a4aa7f91548bad5b4103b747256e8",
"title": "M4GT-Bench: Evaluation Benchmark for Black-Box Machine-Generated Text Detection",
"abstract": "The advent of Large Language Models (LLMs) has brought an unprecedented surge in machine-generated text (MGT) across diverse channels. This raises legi... | 3e5d4cad3657e1ad32d39bf21c1ca4307836cc3472d363f08d0ee0b0af480fb7 | c59628de894a4aa7f91548bad5b4103b747256e8 | M4GT-Bench: Evaluation Benchmark for Black-Box Machine-Generated Text Detection | The advent of Large Language Models (LLMs) has brought an unprecedented surge in machine-generated text (MGT) across diverse channels. This raises legitimate concerns about its potential misuse and societal implications. The need to identify and differentiate such content from genuine human-generated text is critical i... | 38 | 13 | 0 | 0 |
41 | 41/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/41/aim_paper/aim_paper.txt | {
"id": "a915b3b2d0fdfefbf038a8746ceaf001859f3431",
"title": "CoGenesis: A Framework Collaborating Large and Small Language Models for Secure Context-Aware Instruction Following",
"abstract": "With the advancement of language models (LMs), their exposure to private data is increasingly inevitable, and their... | 653b7d02775ea7d26fe0b704bb42e218312f8592936cf7d06ebc8271fbb6b4b2 | a915b3b2d0fdfefbf038a8746ceaf001859f3431 | CoGenesis: A Framework Collaborating Large and Small Language Models for Secure Context-Aware Instruction Following | With the advancement of language models (LMs), their exposure to private data is increasingly inevitable, and their deployment (especially for smaller ones) on personal devices, such as PCs and smartphones, has become a prevailing trend. In contexts laden with user information, enabling models to both safeguard user pr... | 34 | 5 | 15 | 15 |
42 | 42/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/42/aim_paper/aim_paper.txt | {
"id": "0f51d47871d99cda3e9eaf4ae1c9c7025ae76325",
"title": "A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains",
"abstract": "Prompting language models to provide step-by-step answers (e.g.,\"Chain-of-Thought\") is the prominent approach for complex reasonin... | 8a5b89073f0c63d97f8d4d6c8f662d79f43e39212d0a5688ddafa8760e76f4e3 | 0f51d47871d99cda3e9eaf4ae1c9c7025ae76325 | A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains | Prompting language models to provide step-by-step answers (e.g.,"Chain-of-Thought") is the prominent approach for complex reasoning tasks, where more accurate reasoning chains typically improve downstream task performance. Recent literature discusses automatic methods to verify reasoning to evaluate and improve their c... | 39 | 8 | 15 | 15 |
43 | 43/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/43/aim_paper/aim_paper.txt | {
"id": "9b3a76d3a5f48080c31cd123f9d3899081d32577",
"title": "FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models",
"abstract": "The ability to follow instructions is crucial for Large Language Models (LLMs) to handle various real-world applications. Existing be... | 97ec28ae80f0c923f4120b68277e434e6e25963d61441aab91e7ec9a76863fe3 | 9b3a76d3a5f48080c31cd123f9d3899081d32577 | FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models | The ability to follow instructions is crucial for Large Language Models (LLMs) to handle various real-world applications. Existing benchmarks primarily focus on evaluating pure response quality, rather than assessing whether the response follows constraints stated in the instruction. To fill this research gap, in this ... | 41 | 10 | 0 | 0 |
44 | 44/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/44/aim_paper/aim_paper.txt | {
"id": "90213971da84c974bc7502b1112a0ee8a0a33601",
"title": "Who Wrote this Code? Watermarking for Code Generation",
"abstract": "Since the remarkable generation performance of large language models raised ethical and legal concerns, approaches to detect machine-generated text by embedding watermarks are b... | aa8329d19fa442ca0bc4158957483425019d0fe9dca5c89f83270530cb3f6de6 | 90213971da84c974bc7502b1112a0ee8a0a33601 | Who Wrote this Code? Watermarking for Code Generation | Since the remarkable generation performance of large language models raised ethical and legal concerns, approaches to detect machine-generated text by embedding watermarks are being developed. However, we discover that the existing works fail to function appropriately in code generation tasks due to the task's nature o... | 55 | 8 | 15 | 15 |
45 | 45/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/45/aim_paper/aim_paper.txt | {
"id": "e81c707040ce604c7102cfe14d78b72385c17b68",
"title": "MapCoder: Multi-Agent Code Generation for Competitive Problem Solving",
"abstract": "Code synthesis, which requires a deep understanding of complex natural language problem descriptions, generation of code instructions for complex algorithms and ... | 1ba750dd6200b990c6bdb274f56efeb1cb0ad2e2e7c576bda37b1ab6b4c37c1e | e81c707040ce604c7102cfe14d78b72385c17b68 | MapCoder: Multi-Agent Code Generation for Competitive Problem Solving | Code synthesis, which requires a deep understanding of complex natural language problem descriptions, generation of code instructions for complex algorithms and data structures, and the successful execution of comprehensive unit tests, presents a significant challenge. While large language models (LLMs) demonstrate imp... | 37 | 3 | 15 | 15 |
46 | 46/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/46/aim_paper/aim_paper.txt | {
"id": "3e4afde5a9de2c1801da99b8aff5ae05923f256b",
"title": "Are Emergent Abilities in Large Language Models just In-Context Learning?",
"abstract": "Large language models, comprising billions of parameters and pre-trained on extensive web-scale corpora, have been claimed to acquire certain capabilities wi... | ee82ff292283f926134413edd62d85fbfe663cc35aaae900d989696d5665d811 | 3e4afde5a9de2c1801da99b8aff5ae05923f256b | Are Emergent Abilities in Large Language Models just In-Context Learning? | Large language models, comprising billions of parameters and pre-trained on extensive web-scale corpora, have been claimed to acquire certain capabilities without having been specifically trained on them. These capabilities, referred to as"emergent abilities,"have been a driving force in discussions regarding the poten... | 62 | 5 | 0 | 0 |
47 | 47/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/47/aim_paper/aim_paper.txt | {
"id": "c01c7c1f903dfaa78812fb20a6cb2db25e4712e3",
"title": "Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthiness",
"abstract": "We introduce BSDetector, a method for detecting bad and speculative answers from a pretrained Large Language Model by estimating a numer... | 133e395d64471450b11c66ae06fb40ea0a4d742add1b05fac6801370d4b36384 | c01c7c1f903dfaa78812fb20a6cb2db25e4712e3 | Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthiness | We introduce BSDetector, a method for detecting bad and speculative answers from a pretrained Large Language Model by estimating a numeric confidence score for any output it generated. Our uncertainty quantification technique works for any LLM accessible only via a black-box API, whose training data remains unknown. By... | 35 | 1 | 15 | 15 |
48 | 48/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/48/aim_paper/aim_paper.txt | {
"id": "1146d40d3d01427a008a20530269667b8989750c",
"title": "UHGEval: Benchmarking the Hallucination of Chinese Large Language Models via Unconstrained Generation",
"abstract": "Large language models (LLMs) have emerged as pivotal contributors in contemporary natural language processing and are increasingl... | 3ac70a7200841ee0d5538ba6469184f3a9c0f7af142f18645c94ecf26dd67277 | 1146d40d3d01427a008a20530269667b8989750c | UHGEval: Benchmarking the Hallucination of Chinese Large Language Models via Unconstrained Generation | Large language models (LLMs) have emerged as pivotal contributors in contemporary natural language processing and are increasingly being applied across a diverse range of industries. However, these large-scale probabilistic statistical models cannot currently ensure the requisite quality in professional content generat... | 34 | 9 | 0 | 0 |
49 | 49/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/49/aim_paper/aim_paper.txt | {
"id": "1c0b3679919cd0531973fced1a1eb49745d9332d",
"title": "Instruction-tuned Language Models are Better Knowledge Learners",
"abstract": "In order for large language model (LLM)-based assistants to effectively adapt to evolving information needs, it must be possible to update their factual knowledge thro... | 6acc14e1a2856bec6045dd42bdf4b28a677ad77da02b43b2357395df140d2ea2 | 1c0b3679919cd0531973fced1a1eb49745d9332d | Instruction-tuned Language Models are Better Knowledge Learners | In order for large language model (LLM)-based assistants to effectively adapt to evolving information needs, it must be possible to update their factual knowledge through continued training on new data. The standard recipe for doing so involves continued pre-training on new documents followed by instruction-tuning on q... | 50 | 9 | 15 | 15 |
50 | 50/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/50/aim_paper/aim_paper.txt | {
"id": "e68cd5d49a4a1d3a24dab7fac52869aad2bf66a2",
"title": "CodeScope: An Execution-based Multilingual Multitask Multidimensional Benchmark for Evaluating LLMs on Code Understanding and Generation",
"abstract": "Large Language Models (LLMs) have demonstrated remarkable performance on assisting humans in p... | 005844d2855e3dcb698b4193f4f4e7ae449327eb146bec589967c92133f8657f | e68cd5d49a4a1d3a24dab7fac52869aad2bf66a2 | CodeScope: An Execution-based Multilingual Multitask Multidimensional Benchmark for Evaluating LLMs on Code Understanding and Generation | Large Language Models (LLMs) have demonstrated remarkable performance on assisting humans in programming and facilitating programming automation. However, existing benchmarks for evaluating the code understanding and generation capacities of LLMs suffer from severe limitations. First, most benchmarks are insufficient a... | 77 | 8 | 15 | 15 |
51 | 51/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/51/aim_paper/aim_paper.txt | {
"id": "2139e414bdf6a5ea7ec4052d3f65a8d49991494b",
"title": "SafeDecoding: Defending against Jailbreak Attacks via Safety-Aware Decoding",
"abstract": "As large language models (LLMs) become increasingly integrated into real-world applications such as code generation and chatbot assistance, extensive effor... | be862165b6f6cbab68dadd1fb796eae5e64ce9104ed314e6b6f1cff42afabb49 | 2139e414bdf6a5ea7ec4052d3f65a8d49991494b | SafeDecoding: Defending against Jailbreak Attacks via Safety-Aware Decoding | As large language models (LLMs) become increasingly integrated into real-world applications such as code generation and chatbot assistance, extensive efforts have been made to align LLM behavior with human values, including safety. Jailbreak attacks, aiming to provoke unintended and unsafe behaviors from LLMs, remain a... | 40 | 6 | 15 | 15 |
52 | 52/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/52/aim_paper/aim_paper.txt | {
"id": "ac9a9849200a45c9541f5605a1ab2458370b87c4",
"title": "Experiential Co-Learning of Software-Developing Agents",
"abstract": "Recent advancements in large language models (LLMs) have brought significant changes to various domains, especially through LLM-driven autonomous agents. A representative scena... | fd292e7fa508adab28800b69e2508c5a26cb5e7bae9deeaf3a66d1e989e40f10 | ac9a9849200a45c9541f5605a1ab2458370b87c4 | Experiential Co-Learning of Software-Developing Agents | Recent advancements in large language models (LLMs) have brought significant changes to various domains, especially through LLM-driven autonomous agents. A representative scenario is in software development, where LLM agents demonstrate efficient collaboration, task division, and assurance of software quality, markedly... | 44 | 7 | 0 | 0 |
53 | 53/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/53/aim_paper/aim_paper.txt | {
"id": "8b6c00246a0ae34f097aa64af7d9cb35b2b43a30",
"title": "RepCodec: A Speech Representation Codec for Speech Tokenization",
"abstract": "With recent rapid growth of large language models (LLMs), discrete speech tokenization has played an important role for injecting speech into LLMs. However, this discr... | b37b29a33ead006dc2d9e28623912570762668de8c1167bc206e1a4625f4fc03 | 8b6c00246a0ae34f097aa64af7d9cb35b2b43a30 | RepCodec: A Speech Representation Codec for Speech Tokenization | With recent rapid growth of large language models (LLMs), discrete speech tokenization has played an important role for injecting speech into LLMs. However, this discretization gives rise to a loss of information, consequently impairing overall performance. To improve the performance of these discrete speech tokens, we... | 36 | 2 | 15 | 15 |
54 | 54/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/54/aim_paper/aim_paper.txt | {
"id": "a512660b2361a11d7d5f7d3d0b4ca6f793c96010",
"title": "On Measuring Faithfulness or Self-consistency of Natural Language Explanations",
"abstract": "Large language models (LLMs) can explain their predictions through post-hoc or Chain-of-Thought (CoT) explanations. But an LLM could make up reasonably ... | 2c8f8a3e836bfb2839dc601f7d2e173bc0253be30788a2f3894c72a55b92bba0 | a512660b2361a11d7d5f7d3d0b4ca6f793c96010 | On Measuring Faithfulness or Self-consistency of Natural Language Explanations | Large language models (LLMs) can explain their predictions through post-hoc or Chain-of-Thought (CoT) explanations. But an LLM could make up reasonably sounding explanations that are unfaithful to its underlying reasoning. Recent work has designed tests that aim to judge the faithfulness of post-hoc or CoT explanations... | 36 | 2 | 15 | 15 |
55 | 55/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/55/aim_paper/aim_paper.txt | {
"id": "e89ee3f84f1f07229a7ba211bad3465d2c80a325",
"title": "Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key?",
"abstract": "Recent progress in LLMs discussion suggests that multi-agent discussion improves the reasoning abilities of LLMs. In this work, we reevaluate this claim t... | ccc412ebc63d5e5e6c1349e427d7f0186ff612aca191197da902c8f53cedfd03 | e89ee3f84f1f07229a7ba211bad3465d2c80a325 | Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key? | Recent progress in LLMs discussion suggests that multi-agent discussion improves the reasoning abilities of LLMs. In this work, we reevaluate this claim through systematic experiments, where we propose a novel group discussion framework to enrich the set of discussion mechanisms. Interestingly, our results show that a ... | 32 | 5 | 0 | 0 |
56 | 56/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/56/aim_paper/aim_paper.txt | {
"id": "b42e5a92890053ef48f794311c28c45e9fe55ddd",
"title": "Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models",
"abstract": "A pivotal advancement in the progress of large language models (LLMs) is the emergence of the Mixture-of-Experts (MoE) LL... | b74d694f136bc2d09f9ec99e031ecd5d9de77457c4a556bf47aeb6dc93166946 | b42e5a92890053ef48f794311c28c45e9fe55ddd | Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models | A pivotal advancement in the progress of large language models (LLMs) is the emergence of the Mixture-of-Experts (MoE) LLMs. Compared to traditional LLMs, MoE LLMs can achieve higher performance with fewer parameters, but it is still hard to deploy them due to their immense parameter sizes. Different from previous weig... | 28 | 8 | 0 | 0 |
57 | 57/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/57/aim_paper/aim_paper.txt | {
"id": "c3d1832ed0444f75d44116fabbdda891aebc4b01",
"title": "LLaMA Pro: Progressive LLaMA with Block Expansion",
"abstract": "Humans generally acquire new skills without compromising the old; however, the opposite holds for Large Language Models (LLMs), e.g., from LLaMA to CodeLLaMA. To this end, we propos... | bbce54c039c1f2935e9ef31413d297264c95fdb4ac2089717df942a5b61257a0 | c3d1832ed0444f75d44116fabbdda891aebc4b01 | LLaMA Pro: Progressive LLaMA with Block Expansion | Humans generally acquire new skills without compromising the old; however, the opposite holds for Large Language Models (LLMs), e.g., from LLaMA to CodeLLaMA. To this end, we propose a new post-pretraining method for LLMs with an expansion of Transformer blocks. We tune the expanded blocks using only new corpus, effici... | 55 | 8 | 0 | 0 |
58 | 58/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/58/aim_paper/aim_paper.txt | {
"id": "311841075acf5a5b38d807c68fa9f55e4aa274bf",
"title": "A Ship of Theseus: Curious Cases of Paraphrasing in LLM-Generated Texts",
"abstract": "In the realm of text manipulation and linguistic transformation, the question of authorship has been a subject of fascination and philosophical inquiry. Much l... | 0cd6abb52fffdf234034eba980b3ff633cba3f6381676d73aa6ba3e92b3ffefb | 311841075acf5a5b38d807c68fa9f55e4aa274bf | A Ship of Theseus: Curious Cases of Paraphrasing in LLM-Generated Texts | In the realm of text manipulation and linguistic transformation, the question of authorship has been a subject of fascination and philosophical inquiry. Much like the Ship of Theseus paradox, which ponders whether a ship remains the same when each of its original planks is replaced, our research delves into an intrigui... | 42 | 8 | 0 | 0 |
59 | 59/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/59/aim_paper/aim_paper.txt | {
"id": "19261c6ad20c6c1e5585a8afcb88196173cbc8a6",
"title": "WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models",
"abstract": "The rapid advancement of large language models (LLMs) has led to a new era marked by the development of autonomous applications in real-world scenarios, whic... | d25c1c98fa1cce7fef98c457cf63eefc6a1959832d10d694fb2c9011d39b7cc4 | 19261c6ad20c6c1e5585a8afcb88196173cbc8a6 | WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models | The rapid advancement of large language models (LLMs) has led to a new era marked by the development of autonomous applications in real-world scenarios, which drives innovation in creating advanced web agents. Existing web agents typically only handle one input modality and are evaluated only in simplified web simulato... | 34 | 8 | 0 | 0 |
60 | 60/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/60/aim_paper/aim_paper.txt | {
"id": "e4e625f8e8ae5ee82e75de5ad6e07af57cca7f53",
"title": "Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation",
"abstract": "Translating natural language sentences to first-order logic (NL-FOL translation) is a longstanding challenge in the NLP and formal ... | a90f18b877575c6bbd7c1d5d4b6bdb1650515b1f5cd328de14ae45b1df695195 | e4e625f8e8ae5ee82e75de5ad6e07af57cca7f53 | Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation | Translating natural language sentences to first-order logic (NL-FOL translation) is a longstanding challenge in the NLP and formal logic literature. This paper introduces LogicLLaMA, a LLaMA-7B model fine-tuned for NL-FOL translation using LoRA on a single GPU. LogicLLaMA is capable of directly translating natural lang... | 28 | 5 | 0 | 0 |
61 | 61/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/61/aim_paper/aim_paper.txt | {
"id": "fd8c64d0b912795e1cefc0aba4c6d90499132755",
"title": "ARIES: A Corpus of Scientific Paper Edits Made in Response to Peer Reviews",
"abstract": "We introduce the task of automatically revising scientific papers based on peer feedback and release ARIES, a dataset of review comments and their correspon... | f5f7c58e84b29ce514444d731adcf01320e9f44f8cfa8008ede880e7f002fcc9 | fd8c64d0b912795e1cefc0aba4c6d90499132755 | ARIES: A Corpus of Scientific Paper Edits Made in Response to Peer Reviews | We introduce the task of automatically revising scientific papers based on peer feedback and release ARIES, a dataset of review comments and their corresponding paper edits. The data is drawn from real reviewer-author interactions from computer science, and we provide labels linking each reviewer comment to the specifi... | 38 | 7 | 0 | 0 |
62 | 62/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/62/aim_paper/aim_paper.txt | {
"id": "680b7ff9fe0b3667bb4b6ea9039e633f6a813f62",
"title": "The Unreasonable Effectiveness of Easy Training Data for Hard Tasks",
"abstract": "How can we train models to perform well on hard test data when hard training data is by definition difficult to label correctly? This question has been termed the ... | d3ea28d031a0e9acde276fca184e7680b74a9ebf4ba54ef8842f71f310900f24 | 680b7ff9fe0b3667bb4b6ea9039e633f6a813f62 | The Unreasonable Effectiveness of Easy Training Data for Hard Tasks | How can we train models to perform well on hard test data when hard training data is by definition difficult to label correctly? This question has been termed the scalable oversight problem and has drawn increasing attention as language models have continually improved. In this paper, we present the surprising conclusi... | 36 | 4 | 0 | 0 |
63 | 63/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/63/aim_paper/aim_paper.txt | {
"id": "9c60c766d65daa5554949fb3bce2df1ef58a5a16",
"title": "PLUG: Leveraging Pivot Language in Cross-Lingual Instruction Tuning",
"abstract": "Instruction tuning has remarkably advanced large language models (LLMs) in understanding and responding to diverse human instructions. Despite the success in high-... | 56bc77d641df8a6773339b456704aa5de19be4db31b2f1a20462cfef9ac0bb12 | 9c60c766d65daa5554949fb3bce2df1ef58a5a16 | PLUG: Leveraging Pivot Language in Cross-Lingual Instruction Tuning | Instruction tuning has remarkably advanced large language models (LLMs) in understanding and responding to diverse human instructions. Despite the success in high-resource languages, its application in lower-resource ones faces challenges due to the imbalanced foundational abilities of LLMs across different languages, ... | 31 | 7 | 0 | 0 |
64 | 64/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/64/aim_paper/aim_paper.txt | {
"id": "63549bf78e4b1e7e1cec505ce65e6e8f90474f41",
"title": "ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs",
"abstract": "Large Language Models (LLMs) still struggle with natural language reasoning tasks. Motivated by the society of minds (Minsky, 1988), we propose R... | fd43e67859b802ac946d429a9a123cb613f9bd2eea8e7390be7c2f706bb378ad | 63549bf78e4b1e7e1cec505ce65e6e8f90474f41 | ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs | Large Language Models (LLMs) still struggle with natural language reasoning tasks. Motivated by the society of minds (Minsky, 1988), we propose ReConcile, a multi-model multi-agent framework designed as a round table conference among diverse LLM agents. ReConcile enhances collaborative reasoning between LLM agents via ... | 58 | 3 | 15 | 15 |
65 | 65/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/65/aim_paper/aim_paper.txt | {
"id": "462041e29f2e4e8d78b3214ee1a286865bc68721",
"title": "ChiMed-GPT: A Chinese Medical Large Language Model with Full Training Regime and Better Alignment to Human Preferences",
"abstract": "Recently, the increasing demand for superior medical services has highlighted the discrepancies in the medical i... | 6cb47ac44f771fd039fb531d4de611daec0878bcab57e56b40ab77e42501651c | 462041e29f2e4e8d78b3214ee1a286865bc68721 | ChiMed-GPT: A Chinese Medical Large Language Model with Full Training Regime and Better Alignment to Human Preferences | Recently, the increasing demand for superior medical services has highlighted the discrepancies in the medical infrastructure. With big data, especially texts, forming the foundation of medical services, there is an exigent need for effective natural language processing (NLP) solutions tailored to the healthcare domain... | 37 | 5 | 0 | 0 |
66 | 66/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/66/aim_paper/aim_paper.txt | {
"id": "17170575aa8b4fa4e3eef5d366ada706a94dd836",
"title": "LaMP: When Large Language Models Meet Personalization",
"abstract": "This paper highlights the importance of personalization in large language models and introduces the LaMP benchmark -- a novel benchmark for training and evaluating language mode... | e50b6e9a164ae1f44ae0f37a7ca0c0f558debbe39ae0782f7a09d481cbeefae5 | 17170575aa8b4fa4e3eef5d366ada706a94dd836 | LaMP: When Large Language Models Meet Personalization | This paper highlights the importance of personalization in large language models and introduces the LaMP benchmark -- a novel benchmark for training and evaluating language models for producing personalized outputs. LaMP offers a comprehensive evaluation framework with diverse language tasks and multiple entries for ea... | 61 | 4 | 0 | 0 |
67 | 67/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/67/aim_paper/aim_paper.txt | {
"id": "a8798c4a2352a118a369678b164ab4c249004642",
"title": "AboutMe: Using Self-Descriptions in Webpages to Document the Effects of English Pretraining Data Filters",
"abstract": "Large language models' (LLMs) abilities are drawn from their pretraining data, and model development begins with data curation... | 51e94528e7c4bd78f690faad5326170391cdf56a63f2e6d76c04a92e19a9ce81 | a8798c4a2352a118a369678b164ab4c249004642 | AboutMe: Using Self-Descriptions in Webpages to Document the Effects of English Pretraining Data Filters | Large language models' (LLMs) abilities are drawn from their pretraining data, and model development begins with data curation. However, decisions around what data is retained or removed during this initial stage are under-scrutinized. In our work, we ground web text, which is a popular pretraining data source, to its ... | 44 | 7 | 0 | 0 |
68 | 68/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/68/aim_paper/aim_paper.txt | {
"id": "9978f937189a279a726545a734d20558095e7a0a",
"title": "MT-Bench-101: A Fine-Grained Benchmark for Evaluating Large Language Models in Multi-Turn Dialogues",
"abstract": "The advent of Large Language Models (LLMs) has drastically enhanced dialogue systems. However, comprehensively evaluating the dialo... | 3ee284161c2ff599ffedd934a34463f02990a5b4226f54c227414d3410ac6584 | 9978f937189a279a726545a734d20558095e7a0a | MT-Bench-101: A Fine-Grained Benchmark for Evaluating Large Language Models in Multi-Turn Dialogues | The advent of Large Language Models (LLMs) has drastically enhanced dialogue systems. However, comprehensively evaluating the dialogue abilities of LLMs remains a challenge. Previous benchmarks have primarily focused on single-turn dialogues or provided coarse-grained and incomplete assessments of multi-turn dialogues,... | 29 | 9 | 0 | 0 |
69 | 69/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/69/aim_paper/aim_paper.txt | {
"id": "9988a412f879731cb67e536663b8ceeb273f5c34",
"title": "What Evidence Do Language Models Find Convincing?",
"abstract": "Retrieval-augmented language models are being increasingly tasked with subjective, contentious, and conflicting queries such as\"is aspartame linked to cancer\". To resolve these am... | 0535cc67fa9baf1c32d65c63defaa9a7ffea5218540a5adf3ef352b4266f1af9 | 9988a412f879731cb67e536663b8ceeb273f5c34 | What Evidence Do Language Models Find Convincing? | Retrieval-augmented language models are being increasingly tasked with subjective, contentious, and conflicting queries such as"is aspartame linked to cancer". To resolve these ambiguous queries, one must search through a large range of websites and consider"which, if any, of this evidence do I find convincing?". In th... | 32 | 3 | 0 | 0 |
70 | 70/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/70/aim_paper/aim_paper.txt | {
"id": "49ed697a1e5cc5be831e33f2efb7dc61c77d28e7",
"title": "Can LLMs Reason with Rules? Logic Scaffolding for Stress-Testing and Improving LLMs",
"abstract": "Large language models (LLMs) have achieved impressive human-like performance across various reasoning tasks. However, their mastery of underlying i... | f2918f9604dfd80dd700ec5937013089ff34c073a7d49916ff62da13cf659924 | 49ed697a1e5cc5be831e33f2efb7dc61c77d28e7 | Can LLMs Reason with Rules? Logic Scaffolding for Stress-Testing and Improving LLMs | Large language models (LLMs) have achieved impressive human-like performance across various reasoning tasks. However, their mastery of underlying inferential rules still falls short of human capabilities. To investigate this, we propose a logic scaffolding inferential rule generation framework, to construct an inferent... | 26 | 3 | 0 | 0 |
71 | 71/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/71/aim_paper/aim_paper.txt | {
"id": "f95da5b7be2fac2381eb5dfe26dc7dc5bc2d9a90",
"title": "Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents",
"abstract": "Large Language Models (LLMs) have become integral components in various autonomous agent systems. In this study, we present an exploration-based trajectory o... | 92e190a068e692403f1ffeed31642219eaca167579ad33dedac14d9c121dbf1c | f95da5b7be2fac2381eb5dfe26dc7dc5bc2d9a90 | Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents | Large Language Models (LLMs) have become integral components in various autonomous agent systems. In this study, we present an exploration-based trajectory optimization approach, referred to as ETO. This learning method is designed to enhance the performance of open LLM agents. Contrary to previous studies that exclusi... | 33 | 5 | 0 | 0 |
72 | 72/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/72/aim_paper/aim_paper.txt | {
"id": "7ca300c16abbd38382dec5b7ea6809fee570be54",
"title": "ReFT: Reasoning with Reinforced Fine-Tuning",
"abstract": "One way to enhance the reasoning capability of Large Language Models (LLMs) is to conduct Supervised Fine-Tuning (SFT) using Chain-of-Thought (CoT) annotations. This approach does not sho... | 19aa2a59cc676b2befc28c1fcb7a55a91d08150d8040d5d73edf53af8f80c9f1 | 7ca300c16abbd38382dec5b7ea6809fee570be54 | ReFT: Reasoning with Reinforced Fine-Tuning | One way to enhance the reasoning capability of Large Language Models (LLMs) is to conduct Supervised Fine-Tuning (SFT) using Chain-of-Thought (CoT) annotations. This approach does not show sufficiently strong generalization ability, however, because the training only relies on the given CoT data. In math problem-solvin... | 44 | 3 | 0 | 0 |
73 | 73/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/73/aim_paper/aim_paper.txt | {
"id": "74f20c57ac323f5fdd6acbab558eb704253ef4e6",
"title": "PokeMQA: Programmable knowledge editing for Multi-hop Question Answering",
"abstract": "Multi-hop question answering (MQA) is one of the challenging tasks to evaluate machine's comprehension and reasoning abilities, where large language models (L... | 03f206542f6b29848b00633811f5e826d908bef5282441f49b894cbb5e07fef9 | 74f20c57ac323f5fdd6acbab558eb704253ef4e6 | PokeMQA: Programmable knowledge editing for Multi-hop Question Answering | Multi-hop question answering (MQA) is one of the challenging tasks to evaluate machine's comprehension and reasoning abilities, where large language models (LLMs) have widely achieved the human-comparable performance. Due to the dynamics of knowledge facts in real world, knowledge editing has been explored to update mo... | 30 | 6 | 0 | 0 |
74 | 74/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/74/aim_paper/aim_paper.txt | {
"id": "f4bdec0cf595720bc8ee5df2196324bac8f52ab4",
"title": "Full Parameter Fine-tuning for Large Language Models with Limited Resources",
"abstract": "Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP) but demand massive GPU resources for training. Lowering the threshold fo... | 6233c93f1467c4e12f0a04f1b3575cafc0961a4b9c1be3c1567b5891c9fa740f | f4bdec0cf595720bc8ee5df2196324bac8f52ab4 | Full Parameter Fine-tuning for Large Language Models with Limited Resources | Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP) but demand massive GPU resources for training. Lowering the threshold for LLMs training would encourage greater participation from researchers, benefiting both academia and society. While existing approaches have focused on parameter-eff... | 32 | 6 | 0 | 0 |
75 | 75/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/75/aim_paper/aim_paper.txt | {
"id": "acb53707ebc5ce954d9821fc721382d997befbe7",
"title": "M3CoT: A Novel Benchmark for Multi-Domain Multi-step Multi-modal Chain-of-Thought",
"abstract": "Multi-modal Chain-of-Thought (MCoT) requires models to leverage knowledge from both textual and visual modalities for step-by-step reasoning, which g... | e4eba1148590b8c16c5d60ee4b3e7dd1b3c3be228caa8ec85024f22c554f6efd | acb53707ebc5ce954d9821fc721382d997befbe7 | M3CoT: A Novel Benchmark for Multi-Domain Multi-step Multi-modal Chain-of-Thought | Multi-modal Chain-of-Thought (MCoT) requires models to leverage knowledge from both textual and visual modalities for step-by-step reasoning, which gains increasing attention. Nevertheless, the current MCoT benchmark still faces some challenges: (1) absence of visual modal reasoning, (2) single-step visual modal reason... | 27 | 6 | 0 | 0 |
76 | 76/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/76/aim_paper/aim_paper.txt | {
"id": "ec072b1382e8da7480737a8cc5b9ca98dda30c9a",
"title": "Machine Unlearning of Pre-trained Large Language Models",
"abstract": "This study investigates the concept of the `right to be forgotten' within the context of large language models (LLMs). We explore machine unlearning as a pivotal solution, wit... | 39d01d818998f7a59782391a9718b0ff1fe571756713da8ef4e9da5f8682225d | ec072b1382e8da7480737a8cc5b9ca98dda30c9a | Machine Unlearning of Pre-trained Large Language Models | This study investigates the concept of the `right to be forgotten' within the context of large language models (LLMs). We explore machine unlearning as a pivotal solution, with a focus on pre-trained models--a notably under-researched area. Our research delineates a comprehensive framework for machine unlearning in pre... | 48 | 6 | 0 | 0 |
77 | 77/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/77/aim_paper/aim_paper.txt | {
"id": "575f403261d5f99526f0b4dfc8644352d6c4467a",
"title": "DocLLM: A layout-aware generative language model for multimodal document understanding",
"abstract": "Enterprise documents such as forms, invoices, receipts, reports, contracts, and other similar records, often carry rich semantics at the interse... | 1539cfa02b9c692b8834e0610c2a97174ee48c2ce49237b9ecd5a5828e25a0bf | 575f403261d5f99526f0b4dfc8644352d6c4467a | DocLLM: A layout-aware generative language model for multimodal document understanding | Enterprise documents such as forms, invoices, receipts, reports, contracts, and other similar records, often carry rich semantics at the intersection of textual and spatial modalities. The visual cues offered by their complex layouts play a crucial role in comprehending these documents effectively. In this paper, we pr... | 53 | 8 | 0 | 0 |
78 | 78/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/78/aim_paper/aim_paper.txt | {
"id": "e53aa81923958e8e247bb9d09250b8ccf0848513",
"title": "Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards",
"abstract": "Fine-grained control over large language models (LLMs) remains a significant challenge, hindering their adaptabi... | c826b9d149e18c26d60123cf88f4b8a6eed81cd0f243a32b76120273be78b9be | e53aa81923958e8e247bb9d09250b8ccf0848513 | Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards | Fine-grained control over large language models (LLMs) remains a significant challenge, hindering their adaptability to diverse user needs. While Reinforcement Learning from Human Feedback (RLHF) shows promise in aligning LLMs, its reliance on scalar rewards often limits its ability to capture diverse user preferences ... | 62 | 7 | 0 | 0 |
79 | 79/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/79/aim_paper/aim_paper.txt | {
"id": "d9a449e1123ca37375c9977f51b7ea6129905803",
"title": "RAVEL: Evaluating Interpretability Methods on Disentangling Language Model Representations",
"abstract": "Individual neurons participate in the representation of multiple high-level concepts. To what extent can different interpretability methods ... | 6c13029cf95cdf28e575674d18b0f9d94be243e061e4ad4be0ead9e607b5a6d5 | d9a449e1123ca37375c9977f51b7ea6129905803 | RAVEL: Evaluating Interpretability Methods on Disentangling Language Model Representations | Individual neurons participate in the representation of multiple high-level concepts. To what extent can different interpretability methods successfully disentangle these roles? To help address this question, we introduce RAVEL (Resolving Attribute-Value Entanglements in Language Models), a dataset that enables tightly... | 51 | 4 | 0 | 0 |
80 | 80/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/80/aim_paper/aim_paper.txt | {
"id": "3ba0f13d9b24a2dcfdcd861150e33c7d71e6c69e",
"title": "KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extraction",
"abstract": "In this paper, we propose KnowCoder, a Large Language Model (LLM) to conduct Universal Information Extraction (UIE) via code generation. KnowCode... | c0f4cb5d6989b4fd54219b1a09d93bb073f0f4172bcd724a2caefb3f06185f09 | 3ba0f13d9b24a2dcfdcd861150e33c7d71e6c69e | KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extraction | In this paper, we propose KnowCoder, a Large Language Model (LLM) to conduct Universal Information Extraction (UIE) via code generation. KnowCoder aims to develop a kind of unified schema representation that LLMs can easily understand and an effective learning framework that encourages LLMs to follow schemas and extrac... | 34 | 15 | 0 | 0 |
81 | 81/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/81/aim_paper/aim_paper.txt | {
"id": "c19ea4c61ad94644f7e315d4bb547f6c610fc93d",
"title": "TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space",
"abstract": "Large Language Models (LLMs) sometimes suffer from producing hallucinations, especially LLMs may generate untruthful responses despite knowing th... | 7c0ec5cc426aa7ddf7af11612262e04977e23069e1a720ea8e1ce5199e2594de | c19ea4c61ad94644f7e315d4bb547f6c610fc93d | TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space | Large Language Models (LLMs) sometimes suffer from producing hallucinations, especially LLMs may generate untruthful responses despite knowing the correct knowledge. Activating the truthfulness within LLM is the key to fully unlocking LLM's knowledge potential. In this paper, we propose TruthX, an inference-time interv... | 30 | 2 | 15 | 15 |
82 | 82/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/82/aim_paper/aim_paper.txt | {
"id": "f6a141b2ef027033119db5e8e1ab816f28ab1ec7",
"title": "Revisiting Demonstration Selection Strategies in In-Context Learning",
"abstract": "Large language models (LLMs) have shown an impressive ability to perform a wide range of tasks using in-context learning (ICL), where a few examples are used to d... | b2d0fd5bfd4a2ffc93e3d6f16234e65632a18919240d090b89aa496f6db1e61c | f6a141b2ef027033119db5e8e1ab816f28ab1ec7 | Revisiting Demonstration Selection Strategies in In-Context Learning | Large language models (LLMs) have shown an impressive ability to perform a wide range of tasks using in-context learning (ICL), where a few examples are used to describe a task to the model. However, the performance of ICL varies significantly with the choice of demonstrations, and it is still unclear why this happens ... | 31 | 7 | 0 | 0 |
83 | 83/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/83/aim_paper/aim_paper.txt | {
"id": "f48b95576bd50a69937d8bbff0cc42bdb20e49f5",
"title": "MM-SAP: A Comprehensive Benchmark for Assessing Self-Awareness of Multimodal Large Language Models in Perception",
"abstract": "Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in visual p... | 177e20297b8bd1ca1941abd03cb1a7e8c901f075dec5cfd26ce904ef11e78741 | f48b95576bd50a69937d8bbff0cc42bdb20e49f5 | MM-SAP: A Comprehensive Benchmark for Assessing Self-Awareness of Multimodal Large Language Models in Perception | Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in visual perception and understanding. However, these models also suffer from hallucinations, which limit their reliability as AI systems. We believe that these hallucinations are partially due to the models' str... | 22 | 6 | 0 | 0 |
84 | 84/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/84/aim_paper/aim_paper.txt | {
"id": "f9b39a6a7e40986b46f7796f3a805d70d7e3931a",
"title": "SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents",
"abstract": "Graphical User Interface (GUI) agents are designed to automate complex tasks on digital devices, such as smartphones and desktops. Most existing GUI agents interact ... | 6a6d9853b8a7855bbb0ac7ad8558b5ab2c533c3de85f67e3cb2e0e3573f850bf | f9b39a6a7e40986b46f7796f3a805d70d7e3931a | SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents | Graphical User Interface (GUI) agents are designed to automate complex tasks on digital devices, such as smartphones and desktops. Most existing GUI agents interact with the environment through extracted structured data, which can be notably lengthy (e.g., HTML) and occasionally inaccessible (e.g., on desktops). To all... | 42 | 6 | 0 | 0 |
85 | 85/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/85/aim_paper/aim_paper.txt | {
"id": "4ba57555bef02f988f2ed3bab2f102733dc55221",
"title": "Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations",
"abstract": "In this paper, we present an innovative process-oriented math process reward model called \\textbf{Math-Shepherd}, which assigns a reward score to each... | 6ee364a2f1d1e925eb807da078b7fed93276c12a366dcbdc6fdb155cac4aa607 | 4ba57555bef02f988f2ed3bab2f102733dc55221 | Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations | In this paper, we present an innovative process-oriented math process reward model called \textbf{Math-Shepherd}, which assigns a reward score to each step of math problem solutions. The training of Math-Shepherd is achieved using automatically constructed process-wise supervision data, breaking the bottleneck of heavy... | 39 | 9 | 0 | 0 |
86 | 86/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/86/aim_paper/aim_paper.txt | {
"id": "38d64919ba526868a850a0e5f6239d4c474b7e7e",
"title": "Large Language Models are not Fair Evaluators",
"abstract": "In this paper, we uncover a systematic bias in the evaluation paradigm of adopting large language models~(LLMs), e.g., GPT-4, as a referee to score and compare the quality of responses ... | 595b5c7b2f3a14edca60ff6fce4ab1d0459645d355f8b6f42422139d013df6c0 | 38d64919ba526868a850a0e5f6239d4c474b7e7e | Large Language Models are not Fair Evaluators | In this paper, we uncover a systematic bias in the evaluation paradigm of adopting large language models~(LLMs), e.g., GPT-4, as a referee to score and compare the quality of responses generated by candidate models. We find that the quality ranking of candidate responses can be easily hacked by simply altering their or... | 33 | 8 | 0 | 0 |
87 | 87/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/87/aim_paper/aim_paper.txt | {
"id": "14191e9f12913ad8c7ac6e1188682afac04aad09",
"title": "AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling",
"abstract": "We introduce AnyGPT, an any-to-any multimodal language model that utilizes discrete representations for the unified processing of various modalities, including speech, ... | 19b82f1b9438092662654766011ed22f80c52d1e2ef15c5191d511dd4c30f38f | 14191e9f12913ad8c7ac6e1188682afac04aad09 | AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling | We introduce AnyGPT, an any-to-any multimodal language model that utilizes discrete representations for the unified processing of various modalities, including speech, text, images, and music. AnyGPT can be trained stably without any alterations to the current large language model (LLM) architecture or training paradig... | 44 | 15 | 0 | 0 |
88 | 88/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/88/aim_paper/aim_paper.txt | {
"id": "c36f638781953f995eb3079f882174846b9ac16a",
"title": "Do Large Language Models Latently Perform Multi-Hop Reasoning?",
"abstract": "We study whether Large Language Models (LLMs) latently perform multi-hop reasoning with complex prompts such as\"The mother of the singer of 'Superstition' is\". We loo... | 0110eefb9741cce2d8e6b8247e23fb167c96dc43f7d8bf76b7ef16f59abc08e5 | c36f638781953f995eb3079f882174846b9ac16a | Do Large Language Models Latently Perform Multi-Hop Reasoning? | We study whether Large Language Models (LLMs) latently perform multi-hop reasoning with complex prompts such as"The mother of the singer of 'Superstition' is". We look for evidence of a latent reasoning pathway where an LLM (1) latently identifies"the singer of 'Superstition'"as Stevie Wonder, the bridge entity, and (2... | 40 | 4 | 0 | 0 |
89 | 89/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/89/aim_paper/aim_paper.txt | {
"id": "6b86326016fc7b900deac073e119c543daa0bafd",
"title": "Quantifying the Persona Effect in LLM Simulations",
"abstract": "Large language models (LLMs) have shown remarkable promise in simulating human language and behavior. This study investigates how integrating persona variables-demographic, social, ... | c733b5da61c6a382cdfa7583911ceffdff492f4f44f0e4866b3cefac320724ab | 6b86326016fc7b900deac073e119c543daa0bafd | Quantifying the Persona Effect in LLM Simulations | Large language models (LLMs) have shown remarkable promise in simulating human language and behavior. This study investigates how integrating persona variables-demographic, social, and behavioral factors-impacts LLMs' ability to simulate diverse perspectives. We find that persona variables account for<10% variance in a... | 34 | 1 | 15 | 15 |
90 | 90/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/90/aim_paper/aim_paper.txt | {
"id": "2ecf11920fbc331333520a46711df2322b77f8b6",
"title": "Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without the Question?",
"abstract": "Multiple-choice question answering (MCQA) is often used to evaluate large language models (LLMs). To see if MCQA assesses LLMs as intended, ... | 1a963309b8b65f3b125683ba5dd9e27c58ba2d82eb0c64933e4973dc3d6897f8 | 2ecf11920fbc331333520a46711df2322b77f8b6 | Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without the Question? | Multiple-choice question answering (MCQA) is often used to evaluate large language models (LLMs). To see if MCQA assesses LLMs as intended, we probe if LLMs can perform MCQA with choices-only prompts, where models must select the correct answer only from the choices. In three MCQA datasets and four LLMs, this prompt be... | 44 | 3 | 0 | 0 |
91 | 91/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/91/aim_paper/aim_paper.txt | {
"id": "ca261cb681b082e90ca6c7a9d325b4265ed1dc28",
"title": "MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Models",
"abstract": "Large language models (LLMs) have achieved remarkable performance in natural language understanding and generation tasks. However, they often suff... | 3d07801f90585a7a4f405fe9764d0ed06588208d06b63c4d42c46306b0cae003 | ca261cb681b082e90ca6c7a9d325b4265ed1dc28 | MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Models | Large language models (LLMs) have achieved remarkable performance in natural language understanding and generation tasks. However, they often suffer from limitations such as difficulty in incorporating new knowledge, generating hallucinations, and explaining their reasoning process. To address these challenges, we prop... | 33 | 3 | 0 | 0 |
92 | 92/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/92/aim_paper/aim_paper.txt | {
"id": "f65ecb65d00f2e69a49465debfdd78efa0838cec",
"title": "Bridging the Preference Gap between Retrievers and LLMs",
"abstract": "Large Language Models (LLMs) have demonstrated superior results across a wide range of tasks, and Retrieval-augmented Generation (RAG) is an effective way to enhance the perfo... | 5af215d63e7719ba241c2856e9f0066395f45c384a0af3e9ade737ae4e4ed17b | f65ecb65d00f2e69a49465debfdd78efa0838cec | Bridging the Preference Gap between Retrievers and LLMs | Large Language Models (LLMs) have demonstrated superior results across a wide range of tasks, and Retrieval-augmented Generation (RAG) is an effective way to enhance the performance by locating relevant information and placing it into the context window of the LLM. However, the relationship between retrievers and LLMs ... | 27 | 6 | 0 | 0 |
93 | 93/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/93/aim_paper/aim_paper.txt | {
"id": "5ff337e94bb710bab34c340e06b0618612126961",
"title": "Large Language Models Can Learn Temporal Reasoning",
"abstract": "While large language models (LLMs) have demonstrated remarkable reasoning capabilities, they are not without their flaws and inaccuracies. Recent studies have introduced various me... | 8b5f1989cdd32c8f0b41cea59b225bb7a7638b59ae05e179a3334c326a1ebe98 | 5ff337e94bb710bab34c340e06b0618612126961 | Large Language Models Can Learn Temporal Reasoning | While large language models (LLMs) have demonstrated remarkable reasoning capabilities, they are not without their flaws and inaccuracies. Recent studies have introduced various methods to mitigate these limitations. Temporal reasoning (TR), in particular, presents a significant challenge for LLMs due to its reliance o... | 54 | 4 | 0 | 0 |
94 | 94/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/94/aim_paper/aim_paper.txt | {
"id": "aee47d4f45d5c02f79fff62ce4147f0d382cd87e",
"title": "Aligning Large Language Models with Human Preferences through Representation Engineering",
"abstract": "Aligning large language models (LLMs) with human preferences is crucial for enhancing their utility in terms of helpfulness, truthfulness, saf... | 8f117fe67d51641d1768fb150f92bf0e8b37a9ab070ee99f3d7c354478e5449f | aee47d4f45d5c02f79fff62ce4147f0d382cd87e | Aligning Large Language Models with Human Preferences through Representation Engineering | Aligning large language models (LLMs) with human preferences is crucial for enhancing their utility in terms of helpfulness, truthfulness, safety, harmlessness, and interestingness. Existing methods for achieving this alignment often involves employing reinforcement learning from human feedback (RLHF) to fine-tune LLMs... | 30 | 9 | 0 | 0 |
95 | 95/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/95/aim_paper/aim_paper.txt | {
"id": "cfce709a65f90312d2bdc1a6cf0380c19becf694",
"title": "RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models",
"abstract": "Retrieval-augmented generation (RAG) has become a main technique for alleviating hallucinations in large language models (LLMs). Despit... | e646b74707563b48f09d5759d592b4e217ca761ca3006050cf4da0920cdb92dd | cfce709a65f90312d2bdc1a6cf0380c19becf694 | RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models | Retrieval-augmented generation (RAG) has become a main technique for alleviating hallucinations in large language models (LLMs). Despite the integration of RAG, LLMs may still present unsupported or contradictory claims to the retrieved contents. In order to develop effective hallucination prevention strategies under R... | 36 | 5 | 0 | 0 |
96 | 96/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/96/aim_paper/aim_paper.txt | {
"id": "1b387e3fbec0447c8bf2dcee21f6db59cdddf698",
"title": "The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models",
"abstract": "In the era of large language models (LLMs), hallucination (i.e., the tendency to generate factually incorrect content) poses great cha... | e797f793fccd4cfba68ba7e525dfe55118e0857656e68102374386875ff2d5f0 | 1b387e3fbec0447c8bf2dcee21f6db59cdddf698 | The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models | In the era of large language models (LLMs), hallucination (i.e., the tendency to generate factually incorrect content) poses great challenge to trustworthy and reliable deployment of LLMs in real-world applications. To tackle the LLM hallucination, three key questions should be well studied: how to detect hallucination... | 42 | 7 | 0 | 0 |
97 | 97/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/97/aim_paper/aim_paper.txt | {
"id": "b23cf88fd5a99ae68618a3b64d3cd3b3c0c6a65f",
"title": "PRP: Propagating Universal Perturbations to Attack Large Language Model Guard-Rails",
"abstract": "Large language models (LLMs) are typically aligned to be harmless to humans. Unfortunately, recent work has shown that such models are susceptible ... | ab39e88bf28391a33b565fcd0926e56023d92df061a3003ecdd7f0146c16fef4 | b23cf88fd5a99ae68618a3b64d3cd3b3c0c6a65f | PRP: Propagating Universal Perturbations to Attack Large Language Model Guard-Rails | Large language models (LLMs) are typically aligned to be harmless to humans. Unfortunately, recent work has shown that such models are susceptible to automated jailbreak attacks that induce them to generate harmful content. More recent LLMs often incorporate an additional layer of defense, a Guard Model, which is a sec... | 33 | 6 | 0 | 0 |
98 | 98/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/98/aim_paper/aim_paper.txt | {
"id": "8f77307a394f006633c27cca56a345c0879eb67c",
"title": "Measuring Political Bias in Large Language Models: What Is Said and How It Is Said",
"abstract": "We propose to measure political bias in LLMs by analyzing both the content and style of their generated content regarding political issues. Existing... | 2853070710c5b2c76f353025efe232d9a54c603d006ef854fcbc29e7901a85a7 | 8f77307a394f006633c27cca56a345c0879eb67c | Measuring Political Bias in Large Language Models: What Is Said and How It Is Said | We propose to measure political bias in LLMs by analyzing both the content and style of their generated content regarding political issues. Existing benchmarks and measures focus on gender and racial biases. However, political bias exists in LLMs and can lead to polarization and other harms in downstream applications. ... | 49 | 4 | 0 | 0 |
99 | 99/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/99/aim_paper/aim_paper.txt | {
"id": "3597c2eff349260c59e7cb2bff31a31db5cf50fc",
"title": "Fortify the Shortest Stave in Attention: Enhancing Context Awareness of Large Language Models for Effective Tool Use",
"abstract": "In this paper, we demonstrate that an inherent waveform pattern in the attention allocation of large language mode... | de12345177ff470e5df1ac17d580d0079b7e78d2e7cfa9b2c221c0bdaab03ea8 | 3597c2eff349260c59e7cb2bff31a31db5cf50fc | Fortify the Shortest Stave in Attention: Enhancing Context Awareness of Large Language Models for Effective Tool Use | In this paper, we demonstrate that an inherent waveform pattern in the attention allocation of large language models (LLMs) significantly affects their performance in tasks demanding a high degree of context awareness, such as utilizing LLMs for tool-use. Specifically, the crucial information in the context will be pot... | 38 | 8 | 0 | 0 |
100 | 100/aim_paper/aim_paper.txt | https://github.com/ChenShuai00/MAGenIdeas/blob/5cd3433b58f05784fa28a1724de634a53806671d/dataset/data/acl2024_long/100/aim_paper/aim_paper.txt | {
"id": "59f5ae64b4533ef62a883ad5b6b667a4157a1005",
"title": "Layer-Condensed KV Cache for Efficient Inference of Large Language Models",
"abstract": "Huge memory consumption has been a major bottleneck for deploying high-throughput large language models in real-world applications. In addition to the large ... | 6798dee4eaceaaddb186c293888b8bf41d1f43275e595a3a2f9972f05469b739 | 59f5ae64b4533ef62a883ad5b6b667a4157a1005 | Layer-Condensed KV Cache for Efficient Inference of Large Language Models | Huge memory consumption has been a major bottleneck for deploying high-throughput large language models in real-world applications. In addition to the large number of parameters, the key-value (KV) cache for the attention mechanism in the transformer architecture consumes a significant amount of memory, especially when... | 26 | 2 | 0 | 0 |
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