Instruction: What field is the article from?

Input:
Title: A Unique Training Strategy to Enhance Language Models Capabilities for Health Mention Detection from Social Media Content
Abstract: An ever-increasing amount of social media content requires advanced AI-based
computer programs capable of extracting useful information. Specifically, the
extraction of health-related content from social media is useful for the
development of diverse types of applications including disease spread,
mortality rate prediction, and finding the impact of diverse types of drugs on
diverse types of diseases. Language models are competent in extracting the
syntactic and semantics of text. However, they face a hard time extracting
similar patterns from social media texts. The primary reason for this shortfall
lies in the non-standardized writing style commonly employed by social media
users. Following the need for an optimal language model competent in extracting
useful patterns from social media text, the key goal of this paper is to train
language models in such a way that they learn to derive generalized patterns.
The key goal is achieved through the incorporation of random weighted
perturbation and contrastive learning strategies. On top of a unique training
strategy, a meta predictor is proposed that reaps the benefits of 5 different
language models for discriminating posts of social media text into non-health
and health-related classes. Comprehensive experimentation across 3 public
benchmark datasets reveals that the proposed training strategy improves the
performance of the language models up to 3.87%, in terms of F1-score, as
compared to their performance with traditional training. Furthermore, the
proposed meta predictor outperforms existing health mention classification
predictors across all 3 benchmark datasets.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: NOIR: Neural Signal Operated Intelligent Robots for Everyday Activities
Abstract: We present Neural Signal Operated Intelligent Robots (NOIR), a
general-purpose, intelligent brain-robot interface system that enables humans
to command robots to perform everyday activities through brain signals. Through
this interface, humans communicate their intended objects of interest and
actions to the robots using electroencephalography (EEG). Our novel system
demonstrates success in an expansive array of 20 challenging, everyday
household activities, including cooking, cleaning, personal care, and
entertainment. The effectiveness of the system is improved by its synergistic
integration of robot learning algorithms, allowing for NOIR to adapt to
individual users and predict their intentions. Our work enhances the way humans
interact with robots, replacing traditional channels of interaction with
direct, neural communication. Project website: https://noir-corl.github.io/.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: InstanT: Semi-supervised Learning with Instance-dependent Thresholds
Abstract: Semi-supervised learning (SSL) has been a fundamental challenge in machine
learning for decades. The primary family of SSL algorithms, known as
pseudo-labeling, involves assigning pseudo-labels to confident unlabeled
instances and incorporating them into the training set. Therefore, the
selection criteria of confident instances are crucial to the success of SSL.
Recently, there has been growing interest in the development of SSL methods
that use dynamic or adaptive thresholds. Yet, these methods typically apply the
same threshold to all samples, or use class-dependent thresholds for instances
belonging to a certain class, while neglecting instance-level information. In
this paper, we propose the study of instance-dependent thresholds, which has
the highest degree of freedom compared with existing methods. Specifically, we
devise a novel instance-dependent threshold function for all unlabeled
instances by utilizing their instance-level ambiguity and the
instance-dependent error rates of pseudo-labels, so instances that are more
likely to have incorrect pseudo-labels will have higher thresholds.
Furthermore, we demonstrate that our instance-dependent threshold function
provides a bounded probabilistic guarantee for the correctness of the
pseudo-labels it assigns.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Bipartite Graph Pre-training for Unsupervised Extractive Summarization with Graph Convolutional Auto-Encoders
Abstract: Pre-trained sentence representations are crucial for identifying significant
sentences in unsupervised document extractive summarization. However, the
traditional two-step paradigm of pre-training and sentence-ranking, creates a
gap due to differing optimization objectives. To address this issue, we argue
that utilizing pre-trained embeddings derived from a process specifically
designed to optimize cohensive and distinctive sentence representations helps
rank significant sentences. To do so, we propose a novel graph pre-training
auto-encoder to obtain sentence embeddings by explicitly modelling
intra-sentential distinctive features and inter-sentential cohesive features
through sentence-word bipartite graphs. These pre-trained sentence
representations are then utilized in a graph-based ranking algorithm for
unsupervised summarization. Our method produces predominant performance for
unsupervised summarization frameworks by providing summary-worthy sentence
representations. It surpasses heavy BERT- or RoBERTa-based sentence
representations in downstream tasks.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: IndoToD: A Multi-Domain Indonesian Benchmark For End-to-End Task-Oriented Dialogue Systems
Abstract: Task-oriented dialogue (ToD) systems have been mostly created for
high-resource languages, such as English and Chinese. However, there is a need
to develop ToD systems for other regional or local languages to broaden their
ability to comprehend the dialogue contexts in various languages. This paper
introduces IndoToD, an end-to-end multi domain ToD benchmark in Indonesian. We
extend two English ToD datasets to Indonesian, comprising four different
domains by delexicalization to efficiently reduce the size of annotations. To
ensure a high-quality data collection, we hire native speakers to manually
translate the dialogues. Along with the original English datasets, these new
Indonesian datasets serve as an effective benchmark for evaluating Indonesian
and English ToD systems as well as exploring the potential benefits of
cross-lingual and bilingual transfer learning approaches.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Improving Cross-Domain Hate Speech Generalizability with Emotion Knowledge
Abstract: Reliable automatic hate speech (HS) detection systems must adapt to the
in-flow of diverse new data to curtail hate speech. However, hate speech
detection systems commonly lack generalizability in identifying hate speech
dissimilar to data used in training, impeding their robustness in real-world
deployments. In this work, we propose a hate speech generalization framework
that leverages emotion knowledge in a multitask architecture to improve the
generalizability of hate speech detection in a cross-domain setting. We
investigate emotion corpora with varying emotion categorical scopes to
determine the best corpus scope for supplying emotion knowledge to foster
generalized hate speech detection. We further assess the relationship between
using pretrained Transformers models adapted for hate speech and its effect on
our emotion-enriched hate speech generalization model. We perform extensive
experiments on six publicly available datasets sourced from different online
domains and show that our emotion-enriched HS detection generalization method
demonstrates consistent generalization improvement in cross-domain evaluation,
increasing generalization performance up to 18.1% and average cross-domain
performance up to 8.5%, according to the F1 measure.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Large language models implicitly learn to straighten neural sentence trajectories to construct a predictive representation of natural language
Abstract: Predicting upcoming events is critical to our ability to interact with our
environment. Transformer models, trained on next-word prediction, appear to
construct representations of linguistic input that can support diverse
downstream tasks. But how does a predictive objective shape such
representations? Inspired by recent work in vision (Henaff et al., 2019), we
test a hypothesis about predictive representations of autoregressive
transformers. In particular, we test whether the neural trajectory of a
sentence becomes progressively straighter as it passes through the network
layers. The key insight is that straighter trajectories should facilitate
prediction via linear extrapolation. We quantify straightness using a
1-dimensional curvature metric, and present four findings in support of the
trajectory straightening hypothesis: i) In trained models, the curvature
decreases from the early to the deeper layers of the network. ii) Models that
perform better on the next-word prediction objective exhibit greater decreases
in curvature, suggesting that this improved ability to straighten sentence
trajectories may be the driver of better language modeling performance. iii)
Given the same linguistic context, the sequences that are generated by the
model have lower curvature than the actual continuations observed in a language
corpus, suggesting that the model favors straighter trajectories for making
predictions. iv) A consistent relationship holds between the average curvature
and the average surprisal of sentences in the deep model layers, such that
sentences with straighter trajectories also have lower surprisal. Importantly,
untrained models do not exhibit these behaviors. In tandem, these results
support the trajectory straightening hypothesis and provide a possible
mechanism for how the geometry of the internal representations of
autoregressive models supports next word prediction.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Rational Sensibility: LLM Enhanced Empathetic Response Generation Guided by Self-presentation Theory
Abstract: Having the ability to empathize is crucial for accurately representing human
behavior during conversations. Despite numerous research aim to improve the
cognitive capability of models by incorporating external knowledge, there has
been limited attention on the sensible and rational expression of the
conversation itself, which are crucial components of the cognitive empathy.
Guided by self-presentation theory in sociology, we have designed an innovative
categorical approach that segregates historical dialogues into sensible and
rational sentences and subsequently elucidate the context through the designed
attention mechanism. However, the rational information within the conversation
is restricted and the external knowledge used in previous methods have
limitations of semantic contradiction and narrow vision field. Considering the
impressive performance of LLM in the domain of intelligent agent. We employ
LLaMA2-70b as a rational brain to analyze the profound logical information
maintained in conversations, which assists the model assessing the balance of
sensibility and rationality to produce quality empathetic responses.
Experimental evaluations demonstrate that our method outperforms other
comparable methods on both automatic and human evaluations.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: DUMA: a Dual-Mind Conversational Agent with Fast and Slow Thinking
Abstract: Inspired by the dual-process theory of human cognition, we introduce DUMA, a
novel conversational agent framework that embodies a dual-mind mechanism
through the utilization of two generative Large Language Models (LLMs)
dedicated to fast and slow thinking respectively. The fast thinking model
serves as the primary interface for external interactions and initial response
generation, evaluating the necessity for engaging the slow thinking model based
on the complexity of the complete response. When invoked, the slow thinking
model takes over the conversation, engaging in meticulous planning, reasoning,
and tool utilization to provide a well-analyzed response. This dual-mind
configuration allows for a seamless transition between intuitive responses and
deliberate problem-solving processes based on the situation. We have
constructed a conversational agent to handle online inquiries in the real
estate industry. The experiment proves that our method balances effectiveness
and efficiency, and has a significant improvement compared to the baseline.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Attacking Graph Neural Networks with Bit Flips: Weisfeiler and Lehman Go Indifferent
Abstract: Prior attacks on graph neural networks have mostly focused on graph poisoning
and evasion, neglecting the network's weights and biases. Traditional
weight-based fault injection attacks, such as bit flip attacks used for
convolutional neural networks, do not consider the unique properties of graph
neural networks. We propose the Injectivity Bit Flip Attack, the first bit flip
attack designed specifically for graph neural networks. Our attack targets the
learnable neighborhood aggregation functions in quantized message passing
neural networks, degrading their ability to distinguish graph structures and
losing the expressivity of the Weisfeiler-Lehman test. Our findings suggest
that exploiting mathematical properties specific to certain graph neural
network architectures can significantly increase their vulnerability to bit
flip attacks. Injectivity Bit Flip Attacks can degrade the maximal expressive
Graph Isomorphism Networks trained on various graph property prediction
datasets to random output by flipping only a small fraction of the network's
bits, demonstrating its higher destructive power compared to a bit flip attack
transferred from convolutional neural networks. Our attack is transparent and
motivated by theoretical insights which are confirmed by extensive empirical
results.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning
Abstract: With the rapid development of large language models (LLMs) and their
integration into large multimodal models (LMMs), there has been impressive
progress in zero-shot completion of user-oriented vision-language tasks.
However, a gap remains in the domain of chart image understanding due to the
distinct abstract components in charts. To address this, we introduce a
large-scale MultiModal Chart Instruction (MMC-Instruction) dataset comprising
600k instances supporting diverse tasks and chart types. Leveraging this data,
we develop MultiModal Chart Assistant (MMCA), an LMM that achieves
state-of-the-art performance on existing chart QA benchmarks. Recognizing the
need for a comprehensive evaluation of LMM chart understanding, we also propose
a MultiModal Chart Benchmark (MMC-Benchmark), a comprehensive human-annotated
benchmark with 9 distinct tasks evaluating reasoning capabilities over charts.
Extensive experiments on MMC-Benchmark reveal the limitations of existing LMMs
on correctly interpreting charts, even for the most recent GPT-4V model. Our
work provides an instruction-tuning methodology and benchmark to advance
multimodal understanding of charts.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: MAAIP: Multi-Agent Adversarial Interaction Priors for imitation from fighting demonstrations for physics-based characters
Abstract: Simulating realistic interaction and motions for physics-based characters is
of great interest for interactive applications, and automatic secondary
character animation in the movie and video game industries. Recent works in
reinforcement learning have proposed impressive results for single character
simulation, especially the ones that use imitation learning based techniques.
However, imitating multiple characters interactions and motions requires to
also model their interactions. In this paper, we propose a novel Multi-Agent
Generative Adversarial Imitation Learning based approach that generalizes the
idea of motion imitation for one character to deal with both the interaction
and the motions of the multiple physics-based characters. Two unstructured
datasets are given as inputs: 1) a single-actor dataset containing motions of a
single actor performing a set of motions linked to a specific application, and
2) an interaction dataset containing a few examples of interactions between
multiple actors. Based on these datasets, our system trains control policies
allowing each character to imitate the interactive skills associated with each
actor, while preserving the intrinsic style. This approach has been tested on
two different fighting styles, boxing and full-body martial art, to demonstrate
the ability of the method to imitate different styles.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Controllable Text Summarization: Unraveling Challenges, Approaches, and Prospects -- A Survey
Abstract: Generic text summarization approaches often fail to address the specific
intent and needs of individual users. Recently, scholarly attention has turned
to the development of summarization methods that are more closely tailored and
controlled to align with specific objectives and user needs. While a growing
corpus of research is devoted towards a more controllable summarization, there
is no comprehensive survey available that thoroughly explores the diverse
controllable aspects or attributes employed in this context, delves into the
associated challenges, and investigates the existing solutions. In this survey,
we formalize the Controllable Text Summarization (CTS) task, categorize
controllable aspects according to their shared characteristics and objectives,
and present a thorough examination of existing methods and datasets within each
category. Moreover, based on our findings, we uncover limitations and research
gaps, while also delving into potential solutions and future directions for
CTS.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Hallucination Augmented Recitations for Language Models
Abstract: Attribution is a key concept in large language models (LLMs) as it enables
control over information sources and enhances the factuality of LLMs. While
existing approaches utilize open book question answering to improve
attribution, factual datasets may reward language models to recall facts that
they already know from their pretraining data, not attribution. In contrast,
counterfactual open book QA datasets would further improve attribution because
the answer could only be grounded in the given text. We propose Hallucination
Augmented Recitations (HAR) for creating counterfactual datasets by utilizing
hallucination in LLMs to improve attribution. For open book QA as a case study,
we demonstrate that models finetuned with our counterfactual datasets improve
text grounding, leading to better open book QA performance, with up to an 8.0%
increase in F1 score. Our counterfactual dataset leads to significantly better
performance than using humanannotated factual datasets, even with 4x smaller
datasets and 4x smaller models. We observe that improvements are consistent
across various model sizes and datasets, including multi-hop, biomedical, and
adversarial QA datasets.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Multi-Operational Mathematical Derivations in Latent Space
Abstract: This paper investigates the possibility of approximating multiple
mathematical operations in latent space for expression derivation. To this end,
we introduce different multi-operational representation paradigms, modelling
mathematical operations as explicit geometric transformations. By leveraging a
symbolic engine, we construct a large-scale dataset comprising 1.7M derivation
steps stemming from 61K premises and 6 operators, analysing the properties of
each paradigm when instantiated with state-of-the-art neural encoders.
Specifically, we investigate how different encoding mechanisms can approximate
equational reasoning in latent space, exploring the trade-off between learning
different operators and specialising within single operations, as well as the
ability to support multi-step derivations and out-of-distribution
generalisation. Our empirical analysis reveals that the multi-operational
paradigm is crucial for disentangling different operators, while discriminating
the conclusions for a single operation is achievable in the original expression
encoder. Moreover, we show that architectural choices can heavily affect the
training dynamics, structural organisation, and generalisation of the latent
space, resulting in significant variations across paradigms and classes of
encoders.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: The Contemporary Art of Image Search: Iterative User Intent Expansion via Vision-Language Model
Abstract: Image search is an essential and user-friendly method to explore vast
galleries of digital images. However, existing image search methods heavily
rely on proximity measurements like tag matching or image similarity, requiring
precise user inputs for satisfactory results. To meet the growing demand for a
contemporary image search engine that enables accurate comprehension of users'
search intentions, we introduce an innovative user intent expansion framework.
Our framework leverages visual-language models to parse and compose multi-modal
user inputs to provide more accurate and satisfying results. It comprises
two-stage processes: 1) a parsing stage that incorporates a language parsing
module with large language models to enhance the comprehension of textual
inputs, along with a visual parsing module that integrates an interactive
segmentation module to swiftly identify detailed visual elements within images;
and 2) a logic composition stage that combines multiple user search intents
into a unified logic expression for more sophisticated operations in complex
searching scenarios. Moreover, the intent expansion framework enables users to
perform flexible contextualized interactions with the search results to further
specify or adjust their detailed search intents iteratively. We implemented the
framework into an image search system for NFT (non-fungible token) search and
conducted a user study to evaluate its usability and novel properties. The
results indicate that the proposed framework significantly improves users'
image search experience. Particularly the parsing and contextualized
interactions prove useful in allowing users to express their search intents
more accurately and engage in a more enjoyable iterative search experience.

Output: Information Retrieval


Instruction: What field is the article from?

Input:
Title: Simplifying Complex Observation Models in Continuous POMDP Planning with Probabilistic Guarantees and Practice
Abstract: Solving partially observable Markov decision processes (POMDPs) with high
dimensional and continuous observations, such as camera images, is required for
many real life robotics and planning problems. Recent researches suggested
machine learned probabilistic models as observation models, but their use is
currently too computationally expensive for online deployment. We deal with the
question of what would be the implication of using simplified observation
models for planning, while retaining formal guarantees on the quality of the
solution. Our main contribution is a novel probabilistic bound based on a
statistical total variation distance of the simplified model. We show that it
bounds the theoretical POMDP value w.r.t. original model, from the empirical
planned value with the simplified model, by generalizing recent results of
particle-belief MDP concentration bounds. Our calculations can be separated
into offline and online parts, and we arrive at formal guarantees without
having to access the costly model at all during planning, which is also a novel
result. Finally, we demonstrate in simulation how to integrate the bound into
the routine of an existing continuous online POMDP solver.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: GResilience: Trading Off Between the Greenness and the Resilience of Collaborative AI Systems
Abstract: A Collaborative Artificial Intelligence System (CAIS) works with humans in a
shared environment to achieve a common goal. To recover from a disruptive event
that degrades its performance and ensures its resilience, a CAIS may then need
to perform a set of actions either by the system, by the humans, or
collaboratively together. As for any other system, recovery actions may cause
energy adverse effects due to the additional required energy. Therefore, it is
of paramount importance to understand which of the above actions can better
trade-off between resilience and greenness. In this in-progress work, we
propose an approach to automatically evaluate CAIS recovery actions for their
ability to trade-off between the resilience and greenness of the system. We
have also designed an experiment protocol and its application to a real CAIS
demonstrator. Our approach aims to attack the problem from two perspectives: as
a one-agent decision problem through optimization, which takes the decision
based on the score of resilience and greenness, and as a two-agent decision
problem through game theory, which takes the decision based on the payoff
computed for resilience and greenness as two players of a cooperative game.

Output: Software Engineering


Instruction: What field is the article from?

Input:
Title: Rethinking Causal Relationships Learning in Graph Neural Networks
Abstract: Graph Neural Networks (GNNs) demonstrate their significance by effectively
modeling complex interrelationships within graph-structured data. To enhance
the credibility and robustness of GNNs, it becomes exceptionally crucial to
bolster their ability to capture causal relationships. However, despite recent
advancements that have indeed strengthened GNNs from a causal learning
perspective, conducting an in-depth analysis specifically targeting the causal
modeling prowess of GNNs remains an unresolved issue. In order to
comprehensively analyze various GNN models from a causal learning perspective,
we constructed an artificially synthesized dataset with known and controllable
causal relationships between data and labels. The rationality of the generated
data is further ensured through theoretical foundations. Drawing insights from
analyses conducted using our dataset, we introduce a lightweight and highly
adaptable GNN module designed to strengthen GNNs' causal learning capabilities
across a diverse range of tasks. Through a series of experiments conducted on
both synthetic datasets and other real-world datasets, we empirically validate
the effectiveness of the proposed module.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Multi-Agent Learning of Efficient Fulfilment and Routing Strategies in E-Commerce
Abstract: This paper presents an integrated algorithmic framework for minimising
product delivery costs in e-commerce (known as the cost-to-serve or C2S). One
of the major challenges in e-commerce is the large volume of spatio-temporally
diverse orders from multiple customers, each of which has to be fulfilled from
one of several warehouses using a fleet of vehicles. This results in two levels
of decision-making: (i) selection of a fulfillment node for each order
(including the option of deferral to a future time), and then (ii) routing of
vehicles (each of which can carry multiple orders originating from the same
warehouse). We propose an approach that combines graph neural networks and
reinforcement learning to train the node selection and vehicle routing agents.
We include real-world constraints such as warehouse inventory capacity, vehicle
characteristics such as travel times, service times, carrying capacity, and
customer constraints including time windows for delivery. The complexity of
this problem arises from the fact that outcomes (rewards) are driven both by
the fulfillment node mapping as well as the routing algorithms, and are
spatio-temporally distributed. Our experiments show that this algorithmic
pipeline outperforms pure heuristic policies.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Are Large Language Models Temporally Grounded?
Abstract: Are Large language models (LLMs) temporally grounded? Since LLMs cannot
perceive and interact with the environment, it is impossible to answer this
question directly. Instead, we provide LLMs with textual narratives and probe
them with respect to their common-sense knowledge of the structure and duration
of events, their ability to order events along a timeline, and self-consistency
within their temporal model (e.g., temporal relations such as after and before
are mutually exclusive for any pair of events). We evaluate state-of-the-art
LLMs (such as LLaMA 2 and GPT-4) on three tasks reflecting these abilities.
Generally, we find that LLMs lag significantly behind both human performance as
well as small-scale, specialised LMs. In-context learning, instruction tuning,
and chain-of-thought prompting reduce this gap only to a limited degree.
Crucially, LLMs struggle the most with self-consistency, displaying incoherent
behaviour in at least 27.23% of their predictions. Contrary to expectations, we
also find that scaling the model size does not guarantee positive gains in
performance. To explain these results, we study the sources from which LLMs may
gather temporal information: we find that sentence ordering in unlabelled
texts, available during pre-training, is only weakly correlated with event
ordering. Moreover, public instruction tuning mixtures contain few temporal
tasks. Hence, we conclude that current LLMs lack a consistent temporal model of
textual narratives. Code, datasets, and LLM outputs are available at
https://github.com/yfqiu-nlp/temporal-llms.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Next-Step Hint Generation for Introductory Programming Using Large Language Models
Abstract: Large Language Models possess skills such as answering questions, writing
essays or solving programming exercises. Since these models are easily
accessible, researchers have investigated their capabilities and risks for
programming education. This work explores how LLMs can contribute to
programming education by supporting students with automated next-step hints. We
investigate prompt practices that lead to effective next-step hints and use
these insights to build our StAP-tutor. We evaluate this tutor by conducting an
experiment with students, and performing expert assessments. Our findings show
that most LLM-generated feedback messages describe one specific next step and
are personalised to the student's code and approach. However, the hints may
contain misleading information and lack sufficient detail when students
approach the end of the assignment. This work demonstrates the potential for
LLM-generated feedback, but further research is required to explore its
practical implementation.

Output: Computers and Society


Instruction: What field is the article from?

Input:
Title: Using Captum to Explain Generative Language Models
Abstract: Captum is a comprehensive library for model explainability in PyTorch,
offering a range of methods from the interpretability literature to enhance
users' understanding of PyTorch models. In this paper, we introduce new
features in Captum that are specifically designed to analyze the behavior of
generative language models. We provide an overview of the available
functionalities and example applications of their potential for understanding
learned associations within generative language models.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: SKU-Patch: Towards Efficient Instance Segmentation for Unseen Objects in Auto-Store
Abstract: In large-scale storehouses, precise instance masks are crucial for robotic
bin picking but are challenging to obtain. Existing instance segmentation
methods typically rely on a tedious process of scene collection, mask
annotation, and network fine-tuning for every single Stock Keeping Unit (SKU).
This paper presents SKU-Patch, a new patch-guided instance segmentation
solution, leveraging only a few image patches for each incoming new SKU to
predict accurate and robust masks, without tedious manual effort and model
re-training. Technical-wise, we design a novel transformer-based network with
(i) a patch-image correlation encoder to capture multi-level image features
calibrated by patch information and (ii) a patch-aware transformer decoder with
parallel task heads to generate instance masks. Extensive experiments on four
storehouse benchmarks manifest that SKU-Patch is able to achieve the best
performance over the state-of-the-art methods. Also, SKU-Patch yields an
average of nearly 100% grasping success rate on more than 50 unseen SKUs in a
robot-aided auto-store logistic pipeline, showing its effectiveness and
practicality.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: BLT: Can Large Language Models Handle Basic Legal Text?
Abstract: We find that the best publicly available LLMs like GPT-4 and PaLM 2 currently
perform poorly at basic text handling required of lawyers or paralegals, such
as looking up the text at a line of a witness deposition or at a subsection of
a contract. We introduce a benchmark to quantify this poor performance, which
casts into doubt LLMs' current reliability as-is for legal practice. Finetuning
for these tasks brings an older LLM to near-perfect performance on our test set
and also raises performance on a related legal task. This stark result
highlights the need for more domain expertise in LLM training.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Visual In-Context Prompting
Abstract: In-context prompting in large language models (LLMs) has become a prevalent
approach to improve zero-shot capabilities, but this idea is less explored in
the vision domain. Existing visual prompting methods focus on referring
segmentation to segment the most relevant object, falling short of addressing
many generic vision tasks like open-set segmentation and detection. In this
paper, we introduce a universal visual in-context prompting framework for both
tasks. In particular, we build on top of an encoder-decoder architecture, and
develop a versatile prompt encoder to support a variety of prompts like
strokes, boxes, and points. We further enhance it to take an arbitrary number
of reference image segments as the context. Our extensive explorations show
that the proposed visual in-context prompting elicits extraordinary referring
and generic segmentation capabilities to refer and detect, yielding competitive
performance to close-set in-domain datasets and showing promising results on
many open-set segmentation datasets. By joint training on COCO and SA-1B, our
model achieves $57.7$ PQ on COCO and $23.2$ PQ on ADE20K. Code will be
available at https://github.com/UX-Decoder/DINOv.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: NExT-Chat: An LMM for Chat, Detection and Segmentation
Abstract: The development of large language models (LLMs) has greatly advanced the
field of multimodal understanding, leading to the emergence of large multimodal
models (LMMs). In order to enhance the level of visual comprehension, recent
studies have equipped LMMs with region-level understanding capabilities by
representing object bounding box coordinates as a series of text sequences
(pix2seq). In this paper, we introduce a novel paradigm for object location
modeling called pix2emb method, where we ask the LMM to output the location
embeddings and then decode them with different decoders. This paradigm allows
us to use different location formats (such as bounding boxes and masks) in
multimodal conversations. Leveraging the proposed pix2emb method, we train an
LMM named NExT-Chat and demonstrate its capability of handling multiple tasks
like visual grounding, region captioning, and grounded reasoning. Comprehensive
experiments show the effectiveness of our NExT-Chat on various tasks, e.g.,
NExT-Chat (87.7) vs. Shikra (86.9) on POPE-Random, NExT-Chat (68.9) vs. LISA
(67.9) on referring expression segmentation task, and NExT-Chat (79.6) vs.
Kosmos-2 (62.3) on region caption task. The code and model are released at
https://github.com/NExT-ChatV/NExT-Chat.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Offshore Wind Plant Instance Segmentation Using Sentinel-1 Time Series, GIS, and Semantic Segmentation Models
Abstract: Offshore wind farms represent a renewable energy source with a significant
global growth trend, and their monitoring is strategic for territorial and
environmental planning. This study's primary objective is to detect offshore
wind plants at an instance level using semantic segmentation models and
Sentinel-1 time series. The secondary objectives are: (a) to develop a database
consisting of labeled data and S-1 time series; (b) to compare the performance
of five deep semantic segmentation architectures (U-Net, U-Net++, Feature
Pyramid Network - FPN, DeepLabv3+, and LinkNet); (c) develop a novel
augmentation strategy that shuffles the positions of the images within the time
series; (d) investigate different dimensions of time series intervals (1, 5,
10, and 15 images); and (e) evaluate the semantic-to-instance conversion
procedure. LinkNet was the top-performing model, followed by U-Net++ and U-Net,
while FPN and DeepLabv3+ presented the worst results. The evaluation of
semantic segmentation models reveals enhanced Intersection over Union (IoU)
(25%) and F-score metrics (18%) with the augmentation of time series images.
The study showcases the augmentation strategy's capability to mitigate biases
and precisely detect invariant targets. Furthermore, the conversion from
semantic to instance segmentation demonstrates its efficacy in accurately
isolating individual instances within classified regions - simplifying training
data and reducing annotation effort and complexity.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Improving Entropy-Based Test-Time Adaptation from a Clustering View
Abstract: Domain shift is a common problem in the realistic world, where training data
and test data follow different data distributions. To deal with this problem,
fully test-time adaptation (TTA) leverages the unlabeled data encountered
during test time to adapt the model. In particular, Entropy-Based TTA (EBTTA)
methods, which minimize the prediction's entropy on test samples, have shown
great success. In this paper, we introduce a new perspective on the EBTTA,
which interprets these methods from a view of clustering. It is an iterative
algorithm: 1) in the assignment step, the forward process of the EBTTA models
is the assignment of labels for these test samples, and 2) in the updating
step, the backward process is the update of the model via the assigned samples.
Based on the interpretation, we can gain a deeper understanding of EBTTA, where
we show that the entropy loss would further increase the largest probability.
Accordingly, we offer an alternative explanation for why existing EBTTA methods
are sensitive to initial assignments, outliers, and batch size. This
observation can guide us to put forward the improvement of EBTTA. We propose
robust label assignment, weight adjustment, and gradient accumulation to
alleviate the above problems. Experimental results demonstrate that our method
can achieve consistent improvements on various datasets. Code is provided in
the supplementary material.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: NormNet: Scale Normalization for 6D Pose Estimation in Stacked Scenarios
Abstract: Existing Object Pose Estimation (OPE) methods for stacked scenarios are not
robust to changes in object scale. This paper proposes a new 6DoF OPE network
(NormNet) for different scale objects in stacked scenarios. Specifically, each
object's scale is first learned with point-wise regression. Then, all objects
in the stacked scenario are normalized into the same scale through semantic
segmentation and affine transformation. Finally, they are fed into a shared
pose estimator to recover their 6D poses. In addition, we introduce a new
Sim-to-Real transfer pipeline, combining style transfer and domain
randomization. This improves the NormNet's performance on real data even if we
only train it on synthetic data. Extensive experiments demonstrate that the
proposed method achieves state-of-the-art performance on public benchmarks and
the MultiScale dataset we constructed. The real-world experiments show that our
method can robustly estimate the 6D pose of objects at different scales.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: SM70: A Large Language Model for Medical Devices
Abstract: We are introducing SM70, a 70 billion-parameter Large Language Model that is
specifically designed for SpassMed's medical devices under the brand name
'JEE1' (pronounced as G1 and means 'Life'). This large language model provides
more accurate and safe responses to medical-domain questions. To fine-tune
SM70, we used around 800K data entries from the publicly available dataset
MedAlpaca. The Llama2 70B open-sourced model served as the foundation for SM70,
and we employed the QLoRA technique for fine-tuning. The evaluation is
conducted across three benchmark datasets - MEDQA - USMLE, PUBMEDQA, and USMLE
- each representing a unique aspect of medical knowledge and reasoning. The
performance of SM70 is contrasted with other notable LLMs, including Llama2
70B, Clinical Camel 70 (CC70), GPT 3.5, GPT 4, and Med-Palm, to provide a
comparative understanding of its capabilities within the medical domain. Our
results indicate that SM70 outperforms several established models in these
datasets, showcasing its proficiency in handling a range of medical queries,
from fact-based questions derived from PubMed abstracts to complex clinical
decision-making scenarios. The robust performance of SM70, particularly in the
USMLE and PUBMEDQA datasets, suggests its potential as an effective tool in
clinical decision support and medical information retrieval. Despite its
promising results, the paper also acknowledges the areas where SM70 lags behind
the most advanced model, GPT 4, thereby highlighting the need for further
development, especially in tasks demanding extensive medical knowledge and
intricate reasoning.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Low-Precision Mixed-Computation Models for Inference on Edge
Abstract: This paper presents a mixed-computation neural network processing approach
for edge applications that incorporates low-precision (low-width) Posit and
low-precision fixed point (FixP) number systems. This mixed-computation
approach employs 4-bit Posit (Posit4), which has higher precision around zero,
for representing weights with high sensitivity, while it uses 4-bit FixP
(FixP4) for representing other weights. A heuristic for analyzing the
importance and the quantization error of the weights is presented to assign the
proper number system to different weights. Additionally, a gradient
approximation for Posit representation is introduced to improve the quality of
weight updates in the backpropagation process. Due to the high energy
consumption of the fully Posit-based computations, neural network operations
are carried out in FixP or Posit/FixP. An efficient hardware implementation of
a MAC operation with a first Posit operand and FixP for a second operand and
accumulator is presented. The efficacy of the proposed low-precision
mixed-computation approach is extensively assessed on vision and language
models. The results show that, on average, the accuracy of the
mixed-computation is about 1.5% higher than that of FixP with a cost of 0.19%
energy overhead.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Multi-Session Budget Optimization for Forward Auction-based Federated Learning
Abstract: Auction-based Federated Learning (AFL) has emerged as an important research
field in recent years. The prevailing strategies for FL model users (MUs)
assume that the entire team of the required data owners (DOs) for an FL task
must be assembled before training can commence. In practice, an MU can trigger
the FL training process multiple times. DOs can thus be gradually recruited
over multiple FL model training sessions. Existing bidding strategies for AFL
MUs are not designed to handle such scenarios. Therefore, the problem of
multi-session AFL remains open. To address this problem, we propose the
Multi-session Budget Optimization Strategy for forward Auction-based Federated
Learning (MultiBOS-AFL). Based on hierarchical reinforcement learning,
MultiBOS-AFL jointly optimizes inter-session budget pacing and intra-session
bidding for AFL MUs, with the objective of maximizing the total utility.
Extensive experiments on six benchmark datasets show that it significantly
outperforms seven state-of-the-art approaches. On average, MultiBOS-AFL
achieves 12.28% higher utility, 14.52% more data acquired through auctions for
a given budget, and 1.23% higher test accuracy achieved by the resulting FL
model compared to the best baseline. To the best of our knowledge, it is the
first budget optimization decision support method with budget pacing capability
designed for MUs in multi-session forward auction-based federated learning

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Edge-assisted U-Shaped Split Federated Learning with Privacy-preserving for Internet of Things
Abstract: In the realm of the Internet of Things (IoT), deploying deep learning models
to process data generated or collected by IoT devices is a critical challenge.
However, direct data transmission can cause network congestion and inefficient
execution, given that IoT devices typically lack computation and communication
capabilities. Centralized data processing in data centers is also no longer
feasible due to concerns over data privacy and security. To address these
challenges, we present an innovative Edge-assisted U-Shaped Split Federated
Learning (EUSFL) framework, which harnesses the high-performance capabilities
of edge servers to assist IoT devices in model training and optimization
process. In this framework, we leverage Federated Learning (FL) to enable data
holders to collaboratively train models without sharing their data, thereby
enhancing data privacy protection by transmitting only model parameters.
Additionally, inspired by Split Learning (SL), we split the neural network into
three parts using U-shaped splitting for local training on IoT devices. By
exploiting the greater computation capability of edge servers, our framework
effectively reduces overall training time and allows IoT devices with varying
capabilities to perform training tasks efficiently. Furthermore, we proposed a
novel noise mechanism called LabelDP to ensure that data features and labels
can securely resist reconstruction attacks, eliminating the risk of privacy
leakage. Our theoretical analysis and experimental results demonstrate that
EUSFL can be integrated with various aggregation algorithms, maintaining good
performance across different computing capabilities of IoT devices, and
significantly reducing training time and local computation overhead.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Lights out: training RL agents robust to temporary blindness
Abstract: Agents trained with DQN rely on an observation at each timestep to decide
what action to take next. However, in real world applications observations can
change or be missing entirely. Examples of this could be a light bulb breaking
down, or the wallpaper in a certain room changing. While these situations
change the actual observation, the underlying optimal policy does not change.
Because of this we want our agent to continue taking actions until it receives
a (recognized) observation again. To achieve this we introduce a combination of
a neural network architecture that uses hidden representations of the
observations and a novel n-step loss function. Our implementation is able to
withstand location based blindness stretches longer than the ones it was
trained on, and therefore shows robustness to temporary blindness. For access
to our implementation, please email Nathan, Marije, or Pau.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Program-Aided Reasoners (better) Know What They Know
Abstract: Prior work shows that program-aided reasoning, in which large language models
(LLMs) are combined with programs written in programming languages such as
Python, can significantly improve accuracy on various reasoning tasks. However,
while accuracy is essential, it is also important for such reasoners to "know
what they know", which can be quantified through the calibration of the model.
In this paper, we compare the calibration of Program Aided Language Models
(PAL) and text-based Chain-of-thought (COT) prompting techniques over 5
datasets and 2 model types: LLaMA models and OpenAI models. Our results
indicate that PAL leads to improved calibration in 75% of the instances. Our
analysis uncovers that prompting styles that produce lesser diversity in
generations also have more calibrated results, and thus we also experiment with
inducing lower generation diversity using temperature scaling and find that for
certain temperatures, PAL is not only more accurate but is also more calibrated
than COT. Overall, we demonstrate that, in the majority of cases, program-aided
reasoners better know what they know than text-based counterparts.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Unified Batch Normalization: Identifying and Alleviating the Feature Condensation in Batch Normalization and a Unified Framework
Abstract: Batch Normalization (BN) has become an essential technique in contemporary
neural network design, enhancing training stability. Specifically, BN employs
centering and scaling operations to standardize features along the batch
dimension and uses an affine transformation to recover features. Although
standard BN has shown its capability to improve deep neural network training
and convergence, it still exhibits inherent limitations in certain cases. Most
existing techniques that enhance BN consider a single or a few aspects of BN.
In this paper, we first identify problems with BN from a feature perspective
and explore that feature condensation exists in the learning when employing BN,
which negatively affects testing performance. To tackle this problem, we
propose a two-stage unified framework called Unified Batch Normalization (UBN).
In the first stage, we utilize a simple feature condensation threshold to
alleviate the feature condensation, which hinders inappropriate statistic
updates in normalization. In the second stage, we unify various normalization
variants to boost each component of BN. Our experimental results reveal that
UBN significantly enhances performance across different visual backbones and
notably expedites network training convergence, particularly in early training
stages. Notably, our method improved about 3% in top-1 accuracy on ImageNet
classification with large batch sizes, showing the effectiveness of our
approach in real-world scenarios.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: AutoPlanBench: : Automatically generating benchmarks for LLM planners from PDDL
Abstract: LLMs are being increasingly used for planning-style tasks, but their
capabilities for planning and reasoning are poorly understood. We present a
novel method for automatically converting planning benchmarks written in PDDL
into textual descriptions and offer a benchmark dataset created with our
method. We show that while the best LLM planners do well on many planning
tasks, others remain out of reach of current methods.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Combinatorial Stochastic-Greedy Bandit
Abstract: We propose a novel combinatorial stochastic-greedy bandit (SGB) algorithm for
combinatorial multi-armed bandit problems when no extra information other than
the joint reward of the selected set of $n$ arms at each time step $t\in [T]$
is observed. SGB adopts an optimized stochastic-explore-then-commit approach
and is specifically designed for scenarios with a large set of base arms.
Unlike existing methods that explore the entire set of unselected base arms
during each selection step, our SGB algorithm samples only an optimized
proportion of unselected arms and selects actions from this subset. We prove
that our algorithm achieves a $(1-1/e)$-regret bound of
$\mathcal{O}(n^{\frac{1}{3}} k^{\frac{2}{3}} T^{\frac{2}{3}}
\log(T)^{\frac{2}{3}})$ for monotone stochastic submodular rewards, which
outperforms the state-of-the-art in terms of the cardinality constraint $k$.
Furthermore, we empirically evaluate the performance of our algorithm in the
context of online constrained social influence maximization. Our results
demonstrate that our proposed approach consistently outperforms the other
algorithms, increasing the performance gap as $k$ grows.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Instability of computer vision models is a necessary result of the task itself
Abstract: Adversarial examples resulting from instability of current computer vision
models are an extremely important topic due to their potential to compromise
any application. In this paper we demonstrate that instability is inevitable
due to a) symmetries (translational invariance) of the data, b) the categorical
nature of the classification task, and c) the fundamental discrepancy of
classifying images as objects themselves. The issue is further exacerbated by
non-exhaustive labelling of the training data. Therefore we conclude that
instability is a necessary result of how the problem of computer vision is
currently formulated. While the problem cannot be eliminated, through the
analysis of the causes, we have arrived at ways how it can be partially
alleviated. These include i) increasing the resolution of images, ii) providing
contextual information for the image, iii) exhaustive labelling of training
data, and iv) preventing attackers from frequent access to the computer vision
system.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: EHRXQA: A Multi-Modal Question Answering Dataset for Electronic Health Records with Chest X-ray Images
Abstract: Electronic Health Records (EHRs), which contain patients' medical histories
in various multi-modal formats, often overlook the potential for joint
reasoning across imaging and table modalities underexplored in current EHR
Question Answering (QA) systems. In this paper, we introduce EHRXQA, a novel
multi-modal question answering dataset combining structured EHRs and chest
X-ray images. To develop our dataset, we first construct two uni-modal
resources: 1) The MIMIC- CXR-VQA dataset, our newly created medical visual
question answering (VQA) benchmark, specifically designed to augment the
imaging modality in EHR QA, and 2) EHRSQL (MIMIC-IV), a refashioned version of
a previously established table-based EHR QA dataset. By integrating these two
uni-modal resources, we successfully construct a multi-modal EHR QA dataset
that necessitates both uni-modal and cross-modal reasoning. To address the
unique challenges of multi-modal questions within EHRs, we propose a
NeuralSQL-based strategy equipped with an external VQA API. This pioneering
endeavor enhances engagement with multi-modal EHR sources and we believe that
our dataset can catalyze advances in real-world medical scenarios such as
clinical decision-making and research. EHRXQA is available at
https://github.com/baeseongsu/ehrxqa.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: DemoFusion: Democratising High-Resolution Image Generation With No $$$
Abstract: High-resolution image generation with Generative Artificial Intelligence
(GenAI) has immense potential but, due to the enormous capital investment
required for training, it is increasingly centralised to a few large
corporations, and hidden behind paywalls. This paper aims to democratise
high-resolution GenAI by advancing the frontier of high-resolution generation
while remaining accessible to a broad audience. We demonstrate that existing
Latent Diffusion Models (LDMs) possess untapped potential for higher-resolution
image generation. Our novel DemoFusion framework seamlessly extends open-source
GenAI models, employing Progressive Upscaling, Skip Residual, and Dilated
Sampling mechanisms to achieve higher-resolution image generation. The
progressive nature of DemoFusion requires more passes, but the intermediate
results can serve as "previews", facilitating rapid prompt iteration.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: System 2 Attention (is something you might need too)
Abstract: Soft attention in Transformer-based Large Language Models (LLMs) is
susceptible to incorporating irrelevant information from the context into its
latent representations, which adversely affects next token generations. To help
rectify these issues, we introduce System 2 Attention (S2A), which leverages
the ability of LLMs to reason in natural language and follow instructions in
order to decide what to attend to. S2A regenerates the input context to only
include the relevant portions, before attending to the regenerated context to
elicit the final response. In experiments, S2A outperforms standard
attention-based LLMs on three tasks containing opinion or irrelevant
information, QA, math word problems and longform generation, where S2A
increases factuality and objectivity, and decreases sycophancy.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: FLASH-RL: Federated Learning Addressing System and Static Heterogeneity using Reinforcement Learning
Abstract: Federated Learning (FL) has emerged as a promising Machine Learning paradigm,
enabling multiple users to collaboratively train a shared model while
preserving their local data. To minimize computing and communication costs
associated with parameter transfer, it is common practice in FL to select a
subset of clients in each training round. This selection must consider both
system and static heterogeneity. Therefore, we propose FLASH-RL, a framework
that utilizes Double Deep QLearning (DDQL) to address both system and static
heterogeneity in FL. FLASH-RL introduces a new reputation-based utility
function to evaluate client contributions based on their current and past
performances. Additionally, an adapted DDQL algorithm is proposed to expedite
the learning process. Experimental results on MNIST and CIFAR-10 datasets have
shown FLASH-RL's effectiveness in achieving a balanced trade-off between model
performance and end-to-end latency against existing solutions. Indeed, FLASH-RL
reduces latency by up to 24.83% compared to FedAVG and 24.67% compared to
FAVOR. It also reduces the training rounds by up to 60.44% compared to FedAVG
and +76% compared to FAVOR. In fall detection using the MobiAct dataset,
FLASH-RL outperforms FedAVG by up to 2.82% in model's performance and reduces
latency by up to 34.75%. Additionally, FLASH-RL achieves the target performance
faster, with up to a 45.32% reduction in training rounds compared to FedAVG.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Clustered Policy Decision Ranking
Abstract: Policies trained via reinforcement learning (RL) are often very complex even
for simple tasks. In an episode with n time steps, a policy will make n
decisions on actions to take, many of which may appear non-intuitive to the
observer. Moreover, it is not clear which of these decisions directly
contribute towards achieving the reward and how significant their contribution
is. Given a trained policy, we propose a black-box method based on statistical
covariance estimation that clusters the states of the environment and ranks
each cluster according to the importance of decisions made in its states. We
compare our measure against a previous statistical fault localization based
ranking procedure.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: A Fully Data-Driven Approach for Realistic Traffic Signal Control Using Offline Reinforcement Learning
Abstract: The optimization of traffic signal control (TSC) is critical for an efficient
transportation system. In recent years, reinforcement learning (RL) techniques
have emerged as a popular approach for TSC and show promising results for
highly adaptive control. However, existing RL-based methods suffer from notably
poor real-world applicability and hardly have any successful deployments. The
reasons for such failures are mostly due to the reliance on over-idealized
traffic simulators for policy optimization, as well as using unrealistic
fine-grained state observations and reward signals that are not directly
obtainable from real-world sensors. In this paper, we propose a fully
Data-Driven and simulator-free framework for realistic Traffic Signal Control
(D2TSC). Specifically, we combine well-established traffic flow theory with
machine learning to construct a reward inference model to infer the reward
signals from coarse-grained traffic data. With the inferred rewards, we further
propose a sample-efficient offline RL method to enable direct signal control
policy learning from historical offline datasets of real-world intersections.
To evaluate our approach, we collect historical traffic data from a real-world
intersection, and develop a highly customized simulation environment that
strictly follows real data characteristics. We demonstrate through extensive
experiments that our approach achieves superior performance over conventional
and offline RL baselines, and also enjoys much better real-world applicability.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Exploring Sparsity in Graph Transformers
Abstract: Graph Transformers (GTs) have achieved impressive results on various
graph-related tasks. However, the huge computational cost of GTs hinders their
deployment and application, especially in resource-constrained environments.
Therefore, in this paper, we explore the feasibility of sparsifying GTs, a
significant yet under-explored topic. We first discuss the redundancy of GTs
based on the characteristics of existing GT models, and then propose a
comprehensive \textbf{G}raph \textbf{T}ransformer \textbf{SP}arsification
(GTSP) framework that helps to reduce the computational complexity of GTs from
four dimensions: the input graph data, attention heads, model layers, and model
weights. Specifically, GTSP designs differentiable masks for each individual
compressible component, enabling effective end-to-end pruning. We examine our
GTSP through extensive experiments on prominent GTs, including GraphTrans,
Graphormer, and GraphGPS. The experimental results substantiate that GTSP
effectively cuts computational costs, accompanied by only marginal decreases in
accuracy or, in some cases, even improvements. For instance, GTSP yields a
reduction of 30\% in Floating Point Operations while contributing to a 1.8\%
increase in Area Under the Curve accuracy on OGBG-HIV dataset. Furthermore, we
provide several insights on the characteristics of attention heads and the
behavior of attention mechanisms, all of which have immense potential to
inspire future research endeavors in this domain.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: A Graphical Model of Hurricane Evacuation Behaviors
Abstract: Natural disasters such as hurricanes are increasing and causing widespread
devastation. People's decisions and actions regarding whether to evacuate or
not are critical and have a large impact on emergency planning and response.
Our interest lies in computationally modeling complex relationships among
various factors influencing evacuation decisions. We conducted a study on the
evacuation of Hurricane Irma of the 2017 Atlantic hurricane season. The study
was guided by the Protection motivation theory (PMT), a widely-used framework
to understand people's responses to potential threats. Graphical models were
constructed to represent the complex relationships among the factors involved
and the evacuation decision. We evaluated different graphical structures based
on conditional independence tests using Irma data. The final model largely
aligns with PMT. It shows that both risk perception (threat appraisal) and
difficulties in evacuation (coping appraisal) influence evacuation decisions
directly and independently. Certain information received from media was found
to influence risk perception, and through it influence evacuation behaviors
indirectly. In addition, several variables were found to influence both risk
perception and evacuation behaviors directly, including family and friends'
suggestions, neighbors' evacuation behaviors, and evacuation notices from
officials.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Interactive Planning Using Large Language Models for Partially Observable Robotics Tasks
Abstract: Designing robotic agents to perform open vocabulary tasks has been the
long-standing goal in robotics and AI. Recently, Large Language Models (LLMs)
have achieved impressive results in creating robotic agents for performing open
vocabulary tasks. However, planning for these tasks in the presence of
uncertainties is challenging as it requires \enquote{chain-of-thought}
reasoning, aggregating information from the environment, updating state
estimates, and generating actions based on the updated state estimates. In this
paper, we present an interactive planning technique for partially observable
tasks using LLMs. In the proposed method, an LLM is used to collect missing
information from the environment using a robot and infer the state of the
underlying problem from collected observations while guiding the robot to
perform the required actions. We also use a fine-tuned Llama 2 model via
self-instruct and compare its performance against a pre-trained LLM like GPT-4.
Results are demonstrated on several tasks in simulation as well as real-world
environments. A video describing our work along with some results could be
found here.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: Utilitarian Algorithm Configuration
Abstract: We present the first nontrivial procedure for configuring heuristic
algorithms to maximize the utility provided to their end users while also
offering theoretical guarantees about performance. Existing procedures seek
configurations that minimize expected runtime. However, very recent theoretical
work argues that expected runtime minimization fails to capture algorithm
designers' preferences. Here we show that the utilitarian objective also
confers significant algorithmic benefits. Intuitively, this is because mean
runtime is dominated by extremely long runs even when they are incredibly rare;
indeed, even when an algorithm never gives rise to such long runs,
configuration procedures that provably minimize mean runtime must perform a
huge number of experiments to demonstrate this fact. In contrast, utility is
bounded and monotonically decreasing in runtime, allowing for meaningful
empirical bounds on a configuration's performance. This paper builds on this
idea to describe effective and theoretically sound configuration procedures. We
prove upper bounds on the runtime of these procedures that are similar to
theoretical lower bounds, while also demonstrating their performance
empirically.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Better Together: Enhancing Generative Knowledge Graph Completion with Language Models and Neighborhood Information
Abstract: Real-world Knowledge Graphs (KGs) often suffer from incompleteness, which
limits their potential performance. Knowledge Graph Completion (KGC) techniques
aim to address this issue. However, traditional KGC methods are computationally
intensive and impractical for large-scale KGs, necessitating the learning of
dense node embeddings and computing pairwise distances. Generative
transformer-based language models (e.g., T5 and recent KGT5) offer a promising
solution as they can predict the tail nodes directly. In this study, we propose
to include node neighborhoods as additional information to improve KGC methods
based on language models. We examine the effects of this imputation and show
that, on both inductive and transductive Wikidata subsets, our method
outperforms KGT5 and conventional KGC approaches. We also provide an extensive
analysis of the impact of neighborhood on model prediction and show its
importance. Furthermore, we point the way to significantly improve KGC through
more effective neighborhood selection.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Notion of Explainable Artificial Intelligence -- An Empirical Investigation from A Users Perspective
Abstract: The growing attention to artificial intelligence-based applications has led
to research interest in explainability issues. This emerging research attention
on explainable AI (XAI) advocates the need to investigate end user-centric
explainable AI. Thus, this study aims to investigate usercentric explainable AI
and considered recommendation systems as the study context. We conducted focus
group interviews to collect qualitative data on the recommendation system. We
asked participants about the end users' comprehension of a recommended item,
its probable explanation, and their opinion of making a recommendation
explainable. Our findings reveal that end users want a non-technical and
tailor-made explanation with on-demand supplementary information. Moreover, we
also observed users requiring an explanation about personal data usage,
detailed user feedback, and authentic and reliable explanations. Finally, we
propose a synthesized framework that aims at involving the end user in the
development process for requirements collection and validation.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Constrained Hierarchical Monte Carlo Belief-State Planning
Abstract: Optimal plans in Constrained Partially Observable Markov Decision Processes
(CPOMDPs) maximize reward objectives while satisfying hard cost constraints,
generalizing safe planning under state and transition uncertainty.
Unfortunately, online CPOMDP planning is extremely difficult in large or
continuous problem domains. In many large robotic domains, hierarchical
decomposition can simplify planning by using tools for low-level control given
high-level action primitives (options). We introduce Constrained Options Belief
Tree Search (COBeTS) to leverage this hierarchy and scale online search-based
CPOMDP planning to large robotic problems. We show that if primitive option
controllers are defined to satisfy assigned constraint budgets, then COBeTS
will satisfy constraints anytime. Otherwise, COBeTS will guide the search
towards a safe sequence of option primitives, and hierarchical monitoring can
be used to achieve runtime safety. We demonstrate COBeTS in several
safety-critical, constrained partially observable robotic domains, showing that
it can plan successfully in continuous CPOMDPs while non-hierarchical baselines
cannot.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Semantic Generative Augmentations for Few-Shot Counting
Abstract: With the availability of powerful text-to-image diffusion models, recent
works have explored the use of synthetic data to improve image classification
performances. These works show that it can effectively augment or even replace
real data. In this work, we investigate how synthetic data can benefit few-shot
class-agnostic counting. This requires to generate images that correspond to a
given input number of objects. However, text-to-image models struggle to grasp
the notion of count. We propose to rely on a double conditioning of Stable
Diffusion with both a prompt and a density map in order to augment a training
dataset for few-shot counting. Due to the small dataset size, the fine-tuned
model tends to generate images close to the training images. We propose to
enhance the diversity of synthesized images by exchanging captions between
images thus creating unseen configurations of object types and spatial layout.
Our experiments show that our diversified generation strategy significantly
improves the counting accuracy of two recent and performing few-shot counting
models on FSC147 and CARPK.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Leveraging Previous Facial Action Units Knowledge for Emotion Recognition on Faces
Abstract: People naturally understand emotions, thus permitting a machine to do the
same could open new paths for human-computer interaction. Facial expressions
can be very useful for emotion recognition techniques, as these are the biggest
transmitters of non-verbal cues capable of being correlated with emotions.
Several techniques are based on Convolutional Neural Networks (CNNs) to extract
information in a machine learning process. However, simple CNNs are not always
sufficient to locate points of interest on the face that can be correlated with
emotions. In this work, we intend to expand the capacity of emotion recognition
techniques by proposing the usage of Facial Action Units (AUs) recognition
techniques to recognize emotions. This recognition will be based on the Facial
Action Coding System (FACS) and computed by a machine learning system. In
particular, our method expands over EmotiRAM, an approach for multi-cue emotion
recognition, in which we improve over their facial encoding module.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Introduction to Transformers: an NLP Perspective
Abstract: Transformers have dominated empirical machine learning models of natural
language processing. In this paper, we introduce basic concepts of Transformers
and present key techniques that form the recent advances of these models. This
includes a description of the standard Transformer architecture, a series of
model refinements, and common applications. Given that Transformers and related
deep learning techniques might be evolving in ways we have never seen, we
cannot dive into all the model details or cover all the technical areas.
Instead, we focus on just those concepts that are helpful for gaining a good
understanding of Transformers and their variants. We also summarize the key
ideas that impact this field, thereby yielding some insights into the strengths
and limitations of these models.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Combining Transfer Learning with In-context Learning using Blackbox LLMs for Zero-shot Knowledge Base Question Answering
Abstract: We address the zero-shot transfer learning setting for the knowledge base
question answering (KBQA) problem, where a large volume of labeled training
data is available for the source domain, but no such labeled examples are
available for the target domain. Transfer learning for KBQA makes use of large
volumes of unlabeled data in the target in addition to the labeled data in the
source. More recently, few-shot in-context learning using Black-box Large
Language Models (BLLMs) has been adapted for KBQA without considering any
source domain data. In this work, we show how to meaningfully combine these two
paradigms for KBQA so that their benefits add up. Specifically, we preserve the
two stage retrieve-then-generate pipeline of supervised KBQA and introduce
interaction between in-context learning using BLLMs and transfer learning from
the source for both stages. In addition, we propose execution-guided
self-refinement using BLLMs, decoupled from the transfer setting. With the help
of experiments using benchmark datasets GrailQA as the source and WebQSP as the
target, we show that the proposed combination brings significant improvements
to both stages and also outperforms by a large margin state-of-the-art
supervised KBQA models trained on the source. We also show that in the
in-domain setting, the proposed BLLM augmentation significantly outperforms
state-of-the-art supervised models, when the volume of labeled data is limited,
and also outperforms these marginally even when using the entire large training
dataset.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: LayerCollapse: Adaptive compression of neural networks
Abstract: Handling the ever-increasing scale of contemporary deep learning and
transformer-based models poses a significant challenge. Although great strides
have been made in optimizing model compression techniques such as model
architecture search and knowledge distillation, the availability of data and
computational resources remains a considerable hurdle for these optimizations.
This paper introduces LayerCollapse, a novel alternative adaptive model
compression methodology. LayerCollapse works by eliminating non-linearities
within the network and collapsing two consecutive fully connected layers into a
single linear transformation. This approach simultaneously reduces both the
number of layers and the parameter count, thereby enhancing model efficiency.
We also introduce a compression aware regularizer, which compresses the model
in alignment with the dataset quality and model expressiveness, consequently
reducing overfitting across tasks. Our results demonstrate LayerCollapse's
effective compression and regularization capabilities in multiple fine-grained
classification benchmarks, achieving up to 74% post training compression with
minimal accuracy loss. We compare this method with knowledge distillation on
the same target network, showcasing a five-fold increase in computational
efficiency and 8% improvement in overall accuracy on the ImageNet dataset.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Robust Safety Classifier for Large Language Models: Adversarial Prompt Shield
Abstract: Large Language Models' safety remains a critical concern due to their
vulnerability to adversarial attacks, which can prompt these systems to produce
harmful responses. In the heart of these systems lies a safety classifier, a
computational model trained to discern and mitigate potentially harmful,
offensive, or unethical outputs. However, contemporary safety classifiers,
despite their potential, often fail when exposed to inputs infused with
adversarial noise. In response, our study introduces the Adversarial Prompt
Shield (APS), a lightweight model that excels in detection accuracy and
demonstrates resilience against adversarial prompts. Additionally, we propose
novel strategies for autonomously generating adversarial training datasets,
named Bot Adversarial Noisy Dialogue (BAND) datasets. These datasets are
designed to fortify the safety classifier's robustness, and we investigate the
consequences of incorporating adversarial examples into the training process.
Through evaluations involving Large Language Models, we demonstrate that our
classifier has the potential to decrease the attack success rate resulting from
adversarial attacks by up to 60%. This advancement paves the way for the next
generation of more reliable and resilient conversational agents.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Running cognitive evaluations on large language models: The do's and the don'ts
Abstract: In this paper, I describe methodological considerations for studies that aim
to evaluate the cognitive capacities of large language models (LLMs) using
language-based behavioral assessments. Drawing on three case studies from the
literature (a commonsense knowledge benchmark, a theory of mind evaluation, and
a test of syntactic agreement), I describe common pitfalls that might arise
when applying a cognitive test to an LLM. I then list 10 do's and don'ts that
should help design high-quality cognitive evaluations for AI systems. I
conclude by discussing four areas where the do's and don'ts are currently under
active discussion -- prompt sensitivity, cultural and linguistic diversity,
using LLMs as research assistants, and running evaluations on open vs. closed
LLMs. Overall, the goal of the paper is to contribute to the broader discussion
of best practices in the rapidly growing field of AI Psychology.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Propagate & Distill: Towards Effective Graph Learners Using Propagation-Embracing MLPs
Abstract: Recent studies attempted to utilize multilayer perceptrons (MLPs) to solve
semisupervised node classification on graphs, by training a student MLP by
knowledge distillation from a teacher graph neural network (GNN). While
previous studies have focused mostly on training the student MLP by matching
the output probability distributions between the teacher and student models
during distillation, it has not been systematically studied how to inject the
structural information in an explicit and interpretable manner. Inspired by
GNNs that separate feature transformation $T$ and propagation $\Pi$, we
re-frame the distillation process as making the student MLP learn both $T$ and
$\Pi$. Although this can be achieved by applying the inverse propagation
$\Pi^{-1}$ before distillation from the teacher, it still comes with a high
computational cost from large matrix multiplications during training. To solve
this problem, we propose Propagate & Distill (P&D), which propagates the output
of the teacher before distillation, which can be interpreted as an approximate
process of the inverse propagation. We demonstrate that P&D can readily improve
the performance of the student MLP.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: X-Eval: Generalizable Multi-aspect Text Evaluation via Augmented Instruction Tuning with Auxiliary Evaluation Aspects
Abstract: Natural Language Generation (NLG) typically involves evaluating the generated
text in various aspects (e.g., consistency and naturalness) to obtain a
comprehensive assessment. However, multi-aspect evaluation remains challenging
as it may require the evaluator to generalize to any given evaluation aspect
even if it's absent during training. In this paper, we introduce X-Eval, a
two-stage instruction tuning framework to evaluate the text in both seen and
unseen aspects customized by end users. X-Eval consists of two learning stages:
the vanilla instruction tuning stage that improves the model's ability to
follow evaluation instructions, and an enhanced instruction tuning stage that
exploits the connections between fine-grained evaluation aspects to better
assess text quality. To support the training of X-Eval, we collect
AspectInstruct, the first instruction tuning dataset tailored for multi-aspect
NLG evaluation spanning 27 diverse evaluation aspects with 65 tasks. To enhance
task diversity, we devise an augmentation strategy that converts human rating
annotations into diverse forms of NLG evaluation tasks, including scoring,
comparison, ranking, and Boolean question answering. Extensive experiments
across three essential categories of NLG tasks: dialogue generation,
summarization, and data-to-text coupled with 21 aspects in meta-evaluation,
demonstrate that our X-Eval enables even a lightweight language model to
achieve a comparable if not higher correlation with human judgments compared to
the state-of-the-art NLG evaluators, such as GPT-4.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Why LLMs Hallucinate, and How to Get (Evidential) Closure: Perceptual, Intensional, and Extensional Learning for Faithful Natural Language Generation
Abstract: We show that LLMs hallucinate because their output is not constrained to be
synonymous with claims for which they have evidence: a condition that we call
evidential closure. Information about the truth or falsity of sentences is not
statistically identified in the standard neural probabilistic language model
setup, and so cannot be conditioned on to generate new strings. We then show
how to constrain LLMs to produce output that does satisfy evidential closure. A
multimodal LLM must learn about the external world (perceptual learning); it
must learn a mapping from strings to states of the world (extensional
learning); and, to achieve fluency when generalizing beyond a body of evidence,
it must learn mappings from strings to their synonyms (intensional learning).
The output of a unimodal LLM must be synonymous with strings in a validated
evidence set. Finally, we present a heuristic procedure, Learn-Babble-Prune,
that yields faithful output from an LLM by rejecting output that is not
synonymous with claims for which the LLM has evidence.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: An integrated framework for developing and evaluating an automated lecture style assessment system
Abstract: The aim of the work presented in this paper is to develop and evaluate an
integrated system that provides automated lecture style evaluation, allowing
teachers to get instant feedback related to the goodness of their lecturing
style. The proposed system aims to promote improvement of lecture quality, that
could upgrade the overall student learning experience. The proposed application
utilizes specific measurable biometric characteristics, such as facial
expressions, body activity, speech rate and intonation, hand movement, and
facial pose, extracted from a video showing the lecturer from the audience
point of view. Measurable biometric features extracted during a lecture are
combined to provide teachers with a score reflecting lecture style quality both
at frame rate and by providing lecture quality metrics for the whole lecture.
The acceptance of the proposed lecture style evaluation system was evaluated by
chief education officers, teachers and students regarding the functionality,
usefulness of the application, and possible improvements. The results indicate
that participants found the application novel and useful in providing automated
feedback regarding lecture quality. Furthermore, the performance evaluation of
the proposed system was compared with the performance of humans in the task of
lecture style evaluation. Results indicate that the proposed system not only
achieves similar performance to human observers, but in some cases, it
outperforms them.

Output: Computers and Society


Instruction: What field is the article from?

Input:
Title: BioInstruct: Instruction Tuning of Large Language Models for Biomedical Natural Language Processing
Abstract: To enhance the performance of large language models (LLMs) in biomedical
natural language processing (BioNLP) by introducing a domain-specific
instruction dataset and examining its impact when combined with multi-task
learning principles. We created the BioInstruct, comprising 25,005 instructions
to instruction-tune LLMs(LLaMA 1 & 2, 7B & 13B version). The instructions were
created by prompting the GPT-4 language model with three-seed samples randomly
drawn from an 80 human curated instructions. We employed Low-Rank
Adaptation(LoRA) for parameter-efficient fine-tuning. We then evaluated these
instruction-tuned LLMs on several BioNLP tasks, which can be grouped into three
major categories: question answering(QA), information extraction(IE), and text
generation(GEN). We also examined whether categories(e.g., QA, IE, and
generation) of instructions impact model performance. Comparing with LLMs
without instruction-tuned, our instruction-tuned LLMs demonstrated marked
performance gains: 17.3% in QA, 5.7% in IE, and 96% in Generation tasks. Our
7B-parameter instruction-tuned LLaMA 1 model was competitive or even surpassed
other LLMs in the biomedical domain that were also fine-tuned from LLaMA 1 with
vast domain-specific data or a variety of tasks. Our results also show that the
performance gain is significantly higher when instruction fine-tuning is
conducted with closely related tasks. Our findings align with the observations
of multi-task learning, suggesting the synergies between two tasks. The
BioInstruct dataset serves as a valuable resource and instruction tuned LLMs
lead to the best performing BioNLP applications.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: A Comparative Study of AI-Generated (GPT-4) and Human-crafted MCQs in Programming Education
Abstract: There is a constant need for educators to develop and maintain effective
up-to-date assessments. While there is a growing body of research in computing
education on utilizing large language models (LLMs) in generation and
engagement with coding exercises, the use of LLMs for generating programming
MCQs has not been extensively explored. We analyzed the capability of GPT-4 to
produce multiple-choice questions (MCQs) aligned with specific learning
objectives (LOs) from Python programming classes in higher education.
Specifically, we developed an LLM-powered (GPT-4) system for generation of MCQs
from high-level course context and module-level LOs. We evaluated 651
LLM-generated and 449 human-crafted MCQs aligned to 246 LOs from 6 Python
courses. We found that GPT-4 was capable of producing MCQs with clear language,
a single correct choice, and high-quality distractors. We also observed that
the generated MCQs appeared to be well-aligned with the LOs. Our findings can
be leveraged by educators wishing to take advantage of the state-of-the-art
generative models to support MCQ authoring efforts.

Output: Computers and Society


Instruction: What field is the article from?

Input:
Title: CholecTrack20: A Dataset for Multi-Class Multiple Tool Tracking in Laparoscopic Surgery
Abstract: Tool tracking in surgical videos is vital in computer-assisted intervention
for tasks like surgeon skill assessment, safety zone estimation, and
human-machine collaboration during minimally invasive procedures. The lack of
large-scale datasets hampers Artificial Intelligence implementation in this
domain. Current datasets exhibit overly generic tracking formalization, often
lacking surgical context: a deficiency that becomes evident when tools move out
of the camera's scope, resulting in rigid trajectories that hinder realistic
surgical representation. This paper addresses the need for a more precise and
adaptable tracking formalization tailored to the intricacies of endoscopic
procedures by introducing CholecTrack20, an extensive dataset meticulously
annotated for multi-class multi-tool tracking across three perspectives
representing the various ways of considering the temporal duration of a tool
trajectory: (1) intraoperative, (2) intracorporeal, and (3) visibility within
the camera's scope. The dataset comprises 20 laparoscopic videos with over
35,000 frames and 65,000 annotated tool instances with details on spatial
location, category, identity, operator, phase, and surgical visual conditions.
This detailed dataset caters to the evolving assistive requirements within a
procedure.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Monkey: Image Resolution and Text Label Are Important Things for Large Multi-modal Models
Abstract: Large Multimodal Models (LMMs) have shown promise in vision-language tasks
but struggle with high-resolution input and detailed scene understanding.
Addressing these challenges, we introduce Monkey to enhance LMM capabilities.
Firstly, Monkey processes input images by dividing them into uniform patches,
each matching the size (e.g., 448x448) used in the original training of the
well-trained vision encoder. Equipped with individual adapter for each patch,
Monkey can handle higher resolutions up to 1344x896 pixels, enabling the
detailed capture of complex visual information. Secondly, it employs a
multi-level description generation method, enriching the context for
scene-object associations. This two-part strategy ensures more effective
learning from generated data: the higher resolution allows for a more detailed
capture of visuals, which in turn enhances the effectiveness of comprehensive
descriptions. Extensive ablative results validate the effectiveness of our
designs. Additionally, experiments on 18 datasets further demonstrate that
Monkey surpasses existing LMMs in many tasks like Image Captioning and various
Visual Question Answering formats. Specially, in qualitative tests focused on
dense text question answering, Monkey has exhibited encouraging results
compared with GPT4V. Code is available at
https://github.com/Yuliang-Liu/Monkey.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Challenges with unsupervised LLM knowledge discovery
Abstract: We show that existing unsupervised methods on large language model (LLM)
activations do not discover knowledge -- instead they seem to discover whatever
feature of the activations is most prominent. The idea behind unsupervised
knowledge elicitation is that knowledge satisfies a consistency structure,
which can be used to discover knowledge. We first prove theoretically that
arbitrary features (not just knowledge) satisfy the consistency structure of a
particular leading unsupervised knowledge-elicitation method,
contrast-consistent search (Burns et al. - arXiv:2212.03827). We then present a
series of experiments showing settings in which unsupervised methods result in
classifiers that do not predict knowledge, but instead predict a different
prominent feature. We conclude that existing unsupervised methods for
discovering latent knowledge are insufficient, and we contribute sanity checks
to apply to evaluating future knowledge elicitation methods. Conceptually, we
hypothesise that the identification issues explored here, e.g. distinguishing a
model's knowledge from that of a simulated character's, will persist for future
unsupervised methods.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: AlberDICE: Addressing Out-Of-Distribution Joint Actions in Offline Multi-Agent RL via Alternating Stationary Distribution Correction Estimation
Abstract: One of the main challenges in offline Reinforcement Learning (RL) is the
distribution shift that arises from the learned policy deviating from the data
collection policy. This is often addressed by avoiding out-of-distribution
(OOD) actions during policy improvement as their presence can lead to
substantial performance degradation. This challenge is amplified in the offline
Multi-Agent RL (MARL) setting since the joint action space grows exponentially
with the number of agents. To avoid this curse of dimensionality, existing MARL
methods adopt either value decomposition methods or fully decentralized
training of individual agents. However, even when combined with standard
conservatism principles, these methods can still result in the selection of OOD
joint actions in offline MARL. To this end, we introduce AlberDICE, an offline
MARL algorithm that alternatively performs centralized training of individual
agents based on stationary distribution optimization. AlberDICE circumvents the
exponential complexity of MARL by computing the best response of one agent at a
time while effectively avoiding OOD joint action selection. Theoretically, we
show that the alternating optimization procedure converges to Nash policies. In
the experiments, we demonstrate that AlberDICE significantly outperforms
baseline algorithms on a standard suite of MARL benchmarks.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Investigating AI's Challenges in Reasoning and Explanation from a Historical Perspective
Abstract: This paper provides an overview of the intricate relationship between social
dynamics, technological advancements, and pioneering figures in the fields of
cybernetics and artificial intelligence. It explores the impact of
collaboration and interpersonal relationships among key scientists, such as
McCulloch, Wiener, Pitts, and Rosenblatt, on the development of cybernetics and
neural networks. It also discusses the contested attribution of credit for
important innovations like the backpropagation algorithm and the potential
consequences of unresolved debates within emerging scientific domains.
  It emphasizes how interpretive flexibility, public perception, and the
influence of prominent figures can shape the trajectory of a new field. It
highlights the role of funding, media attention, and alliances in determining
the success and recognition of various research approaches. Additionally, it
points out the missed opportunities for collaboration and integration between
symbolic AI and neural network researchers, suggesting that a more unified
approach may be possible in today's era without the historical baggage of past
debates.

Output: Artificial Intelligence