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What field is the article from? | Title: Text Representation Distillation via Information Bottleneck Principle
Abstract: Pre-trained language models (PLMs) have recently shown great success in text
representation field. However, the high computational cost and high-dimensional
representation of PLMs pose significant challenges for practical application... | Computational Linguistics |
What field is the article from? | Title: ML-Bench: Large Language Models Leverage Open-source Libraries for Machine Learning Tasks
Abstract: Large language models have shown promising performance in code generation
benchmarks. However, a considerable divide exists between these benchmark
achievements and their practical applicability, primarily attribu... | Computational Linguistics |
What field is the article from? | Title: CL-MASR: A Continual Learning Benchmark for Multilingual ASR
Abstract: Modern multilingual automatic speech recognition (ASR) systems like Whisper
have made it possible to transcribe audio in multiple languages with a single
model. However, current state-of-the-art ASR models are typically evaluated on
individua... | Computational Linguistics |
What field is the article from? | Title: PsyBench: a balanced and in-depth Psychological Chinese Evaluation Benchmark for Foundation Models
Abstract: As Large Language Models (LLMs) are becoming prevalent in various fields,
there is an urgent need for improved NLP benchmarks that encompass all the
necessary knowledge of individual discipline. Many cont... | Computational Linguistics |
What field is the article from? | Title: C-Disentanglement: Discovering Causally-Independent Generative Factors under an Inductive Bias of Confounder
Abstract: Representation learning assumes that real-world data is generated by a few
semantically meaningful generative factors (i.e., sources of variation) and
aims to discover them in the latent space. ... | Machine Learning |
What field is the article from? | Title: Assume-Guarantee Reinforcement Learning
Abstract: We present a modular approach to \emph{reinforcement learning} (RL) in
environments consisting of simpler components evolving in parallel. A
monolithic view of such modular environments may be prohibitively large to
learn, or may require unrealizable communicatio... | Machine Learning |
What field is the article from? | Title: SDSRA: A Skill-Driven Skill-Recombination Algorithm for Efficient Policy Learning
Abstract: In this paper, we introduce a novel algorithm - the Skill-Driven Skill
Recombination Algorithm (SDSRA) - an innovative framework that significantly
enhances the efficiency of achieving maximum entropy in reinforcement lea... | Machine Learning |
What field is the article from? | Title: From Big to Small Without Losing It All: Text Augmentation with ChatGPT for Efficient Sentiment Analysis
Abstract: In the era of artificial intelligence, data is gold but costly to annotate.
The paper demonstrates a groundbreaking solution to this dilemma using ChatGPT
for text augmentation in sentiment analysis... | Computational Linguistics |
What field is the article from? | Title: ViT-Lens-2: Gateway to Omni-modal Intelligence
Abstract: Aiming to advance AI agents, large foundation models significantly improve
reasoning and instruction execution, yet the current focus on vision and
language neglects the potential of perceiving diverse modalities in open-world
environments. However, the su... | Computer Vision |
What field is the article from? | Title: Accuracy of a Vision-Language Model on Challenging Medical Cases
Abstract: Background: General-purpose large language models that utilize both text and
images have not been evaluated on a diverse array of challenging medical cases.
Methods: Using 934 cases from the NEJM Image Challenge published between 2005
a... | Computer Vision |
What field is the article from? | Title: Context Unlocks Emotions: Text-based Emotion Classification Dataset Auditing with Large Language Models
Abstract: The lack of contextual information in text data can make the annotation
process of text-based emotion classification datasets challenging. As a result,
such datasets often contain labels that fail to... | Computational Linguistics |
What field is the article from? | Title: Its All Graph To Me: Foundational Topology Models with Contrastive Learning on Multiple Domains
Abstract: Representations and embeddings of graph data have been essential in many
domains of research.
The principle benefit of learning such representations is that the
pre-trained model can be fine-tuned on small... | Machine Learning |
What field is the article from? | Title: ReWaRD: Retinal Waves for Pre-Training Artificial Neural Networks Mimicking Real Prenatal Development
Abstract: Computational models trained on a large amount of natural images are the
state-of-the-art to study human vision - usually adult vision. Computational
models of infant vision and its further development... | Computer Vision |
What field is the article from? | Title: A Data-driven and multi-agent decision support system for time slot management at container terminals: A case study for the Port of Rotterdam
Abstract: Controlling the departure time of the trucks from a container hub is
important to both the traffic and the logistics systems. This, however,
requires an intellig... | Artificial Intelligence |
What field is the article from? | Title: ComPEFT: Compression for Communicating Parameter Efficient Updates via Sparsification and Quantization
Abstract: Parameter-efficient fine-tuning (PEFT) techniques make it possible to
efficiently adapt a language model to create "expert" models that specialize to
new tasks or domains. Recent techniques in model m... | Machine Learning |
What field is the article from? | Title: DreamSync: Aligning Text-to-Image Generation with Image Understanding Feedback
Abstract: Despite their wide-spread success, Text-to-Image models (T2I) still struggle
to produce images that are both aesthetically pleasing and faithful to the
user's input text. We introduce DreamSync, a model-agnostic training alg... | Computer Vision |
What field is the article from? | Title: Modality Plug-and-Play: Elastic Modality Adaptation in Multimodal LLMs for Embodied AI
Abstract: Large Language Models (LLMs) are capable of reasoning over diverse input data
modalities through pre-trained encoders. However, the growing diversity of
input data modalities prevents incorporating all modalities int... | Artificial Intelligence |
What field is the article from? | Title: Hand Gesture Classification on Praxis Dataset: Trading Accuracy for Expense
Abstract: In this paper, we investigate hand gesture classifiers that rely upon the
abstracted 'skeletal' data recorded using the RGB-Depth sensor. We focus on
'skeletal' data represented by the body joint coordinates, from the Praxis
da... | Artificial Intelligence |
What field is the article from? | Title: Look Before You Leap: Unveiling the Power of GPT-4V in Robotic Vision-Language Planning
Abstract: In this study, we are interested in imbuing robots with the capability of
physically-grounded task planning. Recent advancements have shown that large
language models (LLMs) possess extensive knowledge useful in rob... | Robotics |
What field is the article from? | Title: Elo Uncovered: Robustness and Best Practices in Language Model Evaluation
Abstract: In Natural Language Processing (NLP), the Elo rating system, originally
designed for ranking players in dynamic games such as chess, is increasingly
being used to evaluate Large Language Models (LLMs) through "A vs B" paired
comp... | Computational Linguistics |
What field is the article from? | Title: Extrinsically-Focused Evaluation of Omissions in Medical Summarization
Abstract: The goal of automated summarization techniques (Paice, 1990; Kupiec et al,
1995) is to condense text by focusing on the most critical information.
Generative large language models (LLMs) have shown to be robust summarizers,
yet trad... | Computational Linguistics |
What field is the article from? | Title: Semantic-Aware Frame-Event Fusion based Pattern Recognition via Large Vision-Language Models
Abstract: Pattern recognition through the fusion of RGB frames and Event streams has
emerged as a novel research area in recent years. Current methods typically
employ backbone networks to individually extract the featur... | Computer Vision |
What field is the article from? | Title: Classification of retail products: From probabilistic ranking to neural networks
Abstract: Food retailing is now on an accelerated path to a success penetration into
the digital market by new ways of value creation at all stages of the consumer
decision process. One of the most important imperatives in this path... | Artificial Intelligence |
What field is the article from? | Title: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion
Abstract: Learning world models can teach an agent how the world works in an
unsupervised manner. Even though it can be viewed as a special case of sequence
modeling, progress for scaling world models on robotic applications such as... | Computer Vision |
What field is the article from? | Title: Finding AI-Generated Faces in the Wild
Abstract: AI-based image generation has continued to rapidly improve, producing
increasingly more realistic images with fewer obvious visual flaws.
AI-generated images are being used to create fake online profiles which in turn
are being used for spam, fraud, and disinforma... | Computer Vision |
What field is the article from? | Title: Latent Feature-Guided Diffusion Models for Shadow Removal
Abstract: Recovering textures under shadows has remained a challenging problem due to
the difficulty of inferring shadow-free scenes from shadow images. In this
paper, we propose the use of diffusion models as they offer a promising
approach to gradually ... | Computer Vision |
What field is the article from? | Title: DCQA: Document-Level Chart Question Answering towards Complex Reasoning and Common-Sense Understanding
Abstract: Visually-situated languages such as charts and plots are omnipresent in
real-world documents. These graphical depictions are human-readable and are
often analyzed in visually-rich documents to address... | Artificial Intelligence |
What field is the article from? | Title: One Self-Configurable Model to Solve Many Abstract Visual Reasoning Problems
Abstract: Abstract Visual Reasoning (AVR) comprises a wide selection of various
problems similar to those used in human IQ tests. Recent years have brought
dynamic progress in solving particular AVR tasks, however, in the contemporary
l... | Artificial Intelligence |
What field is the article from? | Title: An Integrated Framework Integrating Monte Carlo Tree Search and Supervised Learning for Train Timetabling Problem
Abstract: The single-track railway train timetabling problem (TTP) is an important and
complex problem. This article proposes an integrated Monte Carlo Tree Search
(MCTS) computing framework that com... | Machine Learning |
What field is the article from? | 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-relat... | Artificial Intelligence |
What field is the article from? | 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 inter... | Robotics |
What field is the article from? | 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 inc... | Machine Learning |
What field is the article from? | 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-train... | Computational Linguistics |
What field is the article from? | 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 bro... | Computational Linguistics |
What field is the article from? | 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
dissimi... | Computational Linguistics |
What field is the article from? | 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... | Computational Linguistics |
What field is the article from? | 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 e... | Artificial Intelligence |
What field is the article from? | 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 fa... | Computational Linguistics |
What field is the article from? | 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 fo... | Machine Learning |
What field is the article from? | 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.
Howe... | Computational Linguistics |
What field is the article from? | 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... | Computer Vision |
What field is the article from? | 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... | Computational Linguistics |
What field is the article from? | 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 dataset... | Computational Linguistics |
What field is the article from? | 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 operatio... | Machine Learning |
What field is the article from? | 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 sim... | Information Retrieval |
What field is the article from? | 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 p... | Artificial Intelligence |
What field is the article from? | 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 resili... | Software Engineering |
What field is the article from? | 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 t... | Machine Learning |
What field is the article from? | 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-temp... | Artificial Intelligence |
What field is the article from? | 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 com... | Computational Linguistics |
What field is the article from? | 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
... | Computers and Society |
What field is the article from? | 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 speci... | Computational Linguistics |
What field is the article from? | 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
... | Computer Vision |
What field is the article from? | 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. ... | Computational Linguistics |
What field is the article from? | 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,... | Computer Vision |
What field is the article from? | 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 equip... | Computer Vision |
What field is the article from? | 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 primar... | Computer Vision |
What field is the article from? | 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... | Artificial Intelligence |
What field is the article from? | 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, ea... | Computer Vision |
What field is the article from? | 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 r... | Computational Linguistics |
What field is the article from? | 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-... | Machine Learning |
What field is the article from? | 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 ta... | Artificial Intelligence |
What field is the article from? | 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 ... | Machine Learning |
What field is the article from? | 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 t... | Artificial Intelligence |
What field is the article from? | 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... | Artificial Intelligence |
What field is the article from? | 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... | Computer Vision |
What field is the article from? | 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
... | Artificial Intelligence |
What field is the article from? | 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 op... | Machine Learning |
What field is the article from? | 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 inevit... | Computer Vision |
What field is the article from? | 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 underex... | Computational Linguistics |
What field is the article from? | 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, an... | Computer Vision |
What field is the article from? | 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... | Computational Linguistics |
What field is the article from? | 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... | Machine Learning |
What field is the article from? | 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... | Machine Learning |
What field is the article from? | 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 ... | Artificial Intelligence |
What field is the article from? | 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 ... | Machine Learning |
What field is the article from? | 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 c... | Artificial Intelligence |
What field is the article from? | 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 p... | Robotics |
What field is the article from? | 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. Ho... | Artificial Intelligence |
What field is the article from? | 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... | Computational Linguistics |
What field is the article from? | 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 ... | Artificial Intelligence |
What field is the article from? | 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 CP... | Artificial Intelligence |
What field is the article from? | 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 thi... | Computer Vision |
What field is the article from? | 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 t... | Computer Vision |
What field is the article from? | 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... | Computational Linguistics |
What field is the article from? | 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... | Computational Linguistics |
What field is the article from? | 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 know... | Machine Learning |
What field is the article from? | 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, ... | Computational Linguistics |
What field is the article from? | 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 fr... | Artificial Intelligence |
What field is the article from? | 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). ... | Machine Learning |
What field is the article from? | 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, ... | Computational Linguistics |
What field is the article from? | 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... | Computational Linguistics |
What field is the article from? | 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 goodn... | Computers and Society |
What field is the article from? | 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 w... | Computational Linguistics |
What field is the article from? | 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 gene... | Computers and Society |
What field is the article from? | 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 ... | Computer Vision |
What field is the article from? | 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 ... | Computer Vision |
What field is the article from? | 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 ... | Machine Learning |
What field is the article from? | 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
collect... | Machine Learning |
What field is the article from? | 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 im... | Artificial Intelligence |
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