Datasets:

cshuai20
/

acl2024_long

paper_id stringlengths 1 3	source_path stringlengths 25 27	source_url stringlengths 139 141	raw_text stringlengths 877 2.05k	source_sha256 stringlengths 64 64	id stringlengths 40 40	title stringlengths 43 136	abstract stringlengths 708 1.89k	reference_record_count int64 22 128	author_profile_count int64 1 39	idea_file_count int64 0 15	idea_record_count int64 0 15
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

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acl2024_long

Research materials released with Enhancing Research Idea Generation through Combinatorial Innovation and Multi-Agent Iterative Search Strategies, by Shuai Chen and Chengzhi Zhang.

Dataset scope

This release mirrors the preprocessed dataset/data/acl2024_long directory in the MAGenIdeas repository. It contains 144 target-paper directories, their title/abstract records, author role descriptions and associated papers, reference-paper records, generated research ideas, and existing retrieval indexes. It is a selected subset used by MAGenIdeas; it is not the complete ACL 2024 proceedings or a full-text corpus.

The original processing code selects papers with citation count greater than 10, a non-null abstract, author and reference information, and more than 20 references. Author context is assembled from scholarly metadata; the collection code uses ACL Anthology, Semantic Scholar, and OpenAlex. Counts and citation metadata reflect the historical source snapshot, rather than current statistics.

Configurations

Configuration	Records
`papers`	144
`ideas`	693
`reference_papers`	6,153
`author_papers`	25,906
`author_profiles`	952

The default configuration is papers. Each configuration uses a single train split as a storage convention. This release does not define train/validation/test partitions or imply that these records are an independent model-training benchmark.

There are 677 idea files and 693 individual idea records: four idea files each contain a list of five ideas. These lists are expanded into individual rows, preserving their original file path, position within the file, and complete original file text. Missing Keywords fields remain null. Generated ideas are model outputs, rather than human-verified scientific claims or gold labels. The release does not infer a model version, random seed, or quality score that was absent from the source files.

Author and reference-paper configurations preserve occurrences under each target-paper directory. They are not globally deduplicated. paper_id is the original local directory name; id is the scholarly-paper identifier stored in the source. author_local_id is local to a target paper, not a globally unique author identifier. Scientist names in the author descriptions are role labels used by the original project.

Quick start

from datasets import load_dataset

papers = load_dataset("cshuai20/acl2024_long", "papers", split="train")
ideas = load_dataset("cshuai20/acl2024_long", "ideas", split="train")
references = load_dataset("cshuai20/acl2024_long", "reference_papers", split="train")
author_papers = load_dataset("cshuai20/acl2024_long", "author_papers", split="train")
profiles = load_dataset("cshuai20/acl2024_long", "author_profiles", split="train")

Original directory and retrieval indexes

original/acl2024_long.tar.gz contains all 34,408 original files, including 288 .index files and 288 index metadata JSON files. Every file in this archive is byte-identical to its counterpart in the pinned GitHub snapshot. The archive restores the original acl2024_long/ directory layout for the existing MAGenIdeas pipeline. Structured Parquet tables are additional access views and do not replace or modify that archive.

from huggingface_hub import hf_hub_download

archive = hf_hub_download(
    repo_id="cshuai20/acl2024_long",
    repo_type="dataset",
    filename="original/acl2024_long.tar.gz",
)

Existing indexes require the original project's compatible retrieval dependencies. Embeddings are preserved as supplied; this release does not rebuild them. Structured title/abstract tables can be used independently of these indexes.

Provenance and validation

Source repository commit: 5cd3433b58f05784fa28a1724de634a53806671d.

source_manifest.json lists each source path, byte size, Git blob SHA-1, and SHA-256. All 34,408 files were checked against the pinned GitHub tree. Structured tables retain source URLs, hashes, and complete UTF-8 source text. validation.json records the table row counts, schemas, and validation results. The structured views flatten existing fields without editing their source text.

Rights and attribution

The inspected MAGenIdeas source snapshot contains no explicit dataset license. This mirror does not assign a new license or assert additional rights over third-party abstracts and scholarly metadata. The rights and terms of the original publications and source providers continue to apply. Cite the related paper and acknowledge the source repository when describing this dataset.

Intended use and limitations

The materials support reproducing and inspecting the MAGenIdeas research-idea-generation workflow. The target-paper subset is citation- and metadata-filtered, so it should not be treated as a representative sample of all ACL papers. Author profiles and related-paper records may contain incomplete or historical scholarly metadata. Generated ideas should be distinguished from the original publication abstracts in downstream analyses.