id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.11688 | From Isolates to Families: Using Neural Networks for Automated Language
Affiliation | cs.CL | In historical linguistics, the affiliation of languages to a common language
family is traditionally carried out using a complex workflow that relies on
manually comparing individual languages. Large-scale standardized collections
of multilingual wordlists and grammatical language structures might help to
improve thi... |
2502.11689 | Improve LLM-as-a-Judge Ability as a General Ability | cs.CL | LLM-as-a-Judge leverages the generative and reasoning capabilities of large
language models (LLMs) to evaluate LLM responses across diverse scenarios,
providing accurate preference signals. This approach plays a vital role in
aligning LLMs with human values, ensuring ethical and reliable AI outputs that
align with so... |
2502.11697 | MVTokenFlow: High-quality 4D Content Generation using Multiview Token
Flow | cs.CV | In this paper, we present MVTokenFlow for high-quality 4D content creation
from monocular videos. Recent advancements in generative models such as video
diffusion models and multiview diffusion models enable us to create videos or
3D models. However, extending these generative models for dynamic 4D content
creation i... |
2502.11703 | CMQCIC-Bench: A Chinese Benchmark for Evaluating Large Language Models
in Medical Quality Control Indicator Calculation | cs.CL | Medical quality control indicators are essential to assess the qualifications
of healthcare institutions for medical services. With the impressive
performance of large language models (LLMs) like GPT-4 in the medical field,
leveraging these technologies for the Medical Quality Control Indicator
Calculation (MQCIC) pr... |
2502.11705 | LLM Agents Making Agent Tools | cs.CL cs.AI cs.LG cs.MA | Tool use has turned large language models (LLMs) into powerful agents that
can perform complex multi-step tasks by dynamically utilising external software
components. However, these tools must be implemented in advance by human
developers, hindering the applicability of LLM agents in domains which demand
large number... |
2502.11707 | Ad-hoc Concept Forming in the Game Codenames as a Means for Evaluating
Large Language Models | cs.CL | This study utilizes the game Codenames as a benchmarking tool to evaluate
large language models (LLMs) with respect to specific linguistic and cognitive
skills. LLMs play each side of the game, where one side generates a clue word
covering several target words and the other guesses those target words. We
designed var... |
2502.11710 | The Worse The Better: Content-Aware Viewpoint Generation Network for
Projection-related Point Cloud Quality Assessment | cs.CV | Through experimental studies, however, we observed the instability of final
predicted quality scores, which change significantly over different viewpoint
settings. Inspired by the "wooden barrel theory", given the default
content-independent viewpoints of existing projection-related PCQA approaches,
this paper presen... |
2502.11711 | Knowledge-aware contrastive heterogeneous molecular graph learning | cs.LG cs.AI | Molecular representation learning is pivotal in predicting molecular
properties and advancing drug design. Traditional methodologies, which
predominantly rely on homogeneous graph encoding, are limited by their
inability to integrate external knowledge and represent molecular structures
across different levels of gra... |
2502.11712 | Component-aware Unsupervised Logical Anomaly Generation for Industrial
Anomaly Detection | cs.CV | Anomaly detection is critical in industrial manufacturing for ensuring
product quality and improving efficiency in automated processes. The scarcity
of anomalous samples limits traditional detection methods, making anomaly
generation essential for expanding the data repository. However, recent
generative models often... |
2502.11713 | Nonlinearity Cancellation Based on Optimized First Order Perturbative
Kernels | cs.IT math.IT | The potential offered by interference cancellation based on optimized regular
perturbation kernels of the Manakov equation is studied. Theoretical gains of
up to 2.5 dB in effective SNR are demonstrated.
|
2502.11715 | Proactive Depot Discovery: A Generative Framework for Flexible
Location-Routing | cs.LG cs.AI | The Location-Routing Problem (LRP), which combines the challenges of facility
(depot) locating and vehicle route planning, is critically constrained by the
reliance on predefined depot candidates, limiting the solution space and
potentially leading to suboptimal outcomes. Previous research on LRP without
predefined d... |
2502.11718 | ChineseSimpleVQA -- "See the World, Discover Knowledge": A Chinese
Factuality Evaluation for Large Vision Language Models | cs.CL cs.CV | The evaluation of factual accuracy in large vision language models (LVLMs)
has lagged behind their rapid development, making it challenging to fully
reflect these models' knowledge capacity and reliability. In this paper, we
introduce the first factuality-based visual question-answering benchmark in
Chinese, named Ch... |
2502.11720 | Can you pass that tool?: Implications of Indirect Speech in Physical
Human-Robot Collaboration | cs.HC cs.RO | Indirect speech acts (ISAs) are a natural pragmatic feature of human
communication, allowing requests to be conveyed implicitly while maintaining
subtlety and flexibility. Although advancements in speech recognition have
enabled natural language interactions with robots through direct, explicit
commands -- roviding c... |
2502.11721 | Enhancing Recommendation Explanations through User-Centric Refinement | cs.IR | Generating natural language explanations for recommendations has become
increasingly important in recommender systems. Traditional approaches typically
treat user reviews as ground truth for explanations and focus on improving
review prediction accuracy by designing various model architectures. However,
due to limita... |
2502.11723 | Energy-Conscious LLM Decoding: Impact of Text Generation Strategies on
GPU Energy Consumption | cs.AI | Decoding strategies significantly influence the quality and diversity of the
generated texts in large language models (LLMs), yet their impact on
computational resource consumption, particularly GPU energy usage, is
insufficiently studied. This paper investigates the relationship between text
generation decoding meth... |
2502.11724 | Incomplete Modality Disentangled Representation for Ophthalmic Disease
Grading and Diagnosis | cs.CV | Ophthalmologists typically require multimodal data sources to improve
diagnostic accuracy in clinical decisions. However, due to medical device
shortages, low-quality data and data privacy concerns, missing data modalities
are common in real-world scenarios. Existing deep learning methods tend to
address it by learni... |
2502.11725 | Adversarially Robust CLIP Models Can Induce Better (Robust) Perceptual
Metrics | cs.CV cs.LG | Measuring perceptual similarity is a key tool in computer vision. In recent
years perceptual metrics based on features extracted from neural networks with
large and diverse training sets, e.g. CLIP, have become popular. At the same
time, the metrics extracted from features of neural networks are not
adversarially rob... |
2502.11726 | No-reference geometry quality assessment for colorless point clouds via
list-wise rank learning | cs.CV | Geometry quality assessment (GQA) of colorless point clouds is crucial for
evaluating the performance of emerging point cloud-based solutions (e.g.,
watermarking, compression, and 3-Dimensional (3D) reconstruction).
Unfortunately, existing objective GQA approaches are traditional full-reference
metrics, whereas state... |
2502.11728 | Matrix Low-dimensional Qubit Casting Based Quantum Electromagnetic
Transient Network Simulation Program | quant-ph cs.SY eess.SY | In modern power systems, the integration of converter-interfaced generations
requires the development of electromagnetic transient network simulation
programs (EMTP) that can capture rapid fluctuations. However, as the power
system scales, the EMTP's computing complexity increases exponentially, leading
to a curse of... |
2502.11731 | GraphMorph: Tubular Structure Extraction by Morphing Predicted Graphs | cs.CV | Accurately restoring topology is both challenging and crucial in tubular
structure extraction tasks, such as blood vessel segmentation and road network
extraction. Diverging from traditional approaches based on pixel-level
classification, our proposed method, named GraphMorph, focuses on branch-level
features of tubu... |
2502.11733 | Plant in Cupboard, Orange on Table, Book on Shelf. Benchmarking
Practical Reasoning and Situation Modelling in a Text-Simulated Situated
Environment | cs.CL | Large language models (LLMs) have risen to prominence as 'chatbots' for users
to interact via natural language. However, their abilities to capture
common-sense knowledge make them seem promising as language-based planners of
situated or embodied action as well. We have implemented a simple text-based
environment -- ... |
2502.11735 | MT-RAIG: Novel Benchmark and Evaluation Framework for
Retrieval-Augmented Insight Generation over Multiple Tables | cs.CL | Recent advancements in table-based reasoning have expanded beyond
factoid-level QA to address insight-level tasks, where systems should
synthesize implicit knowledge in the table to provide explainable analyses.
Although effective, existing studies remain confined to scenarios where a
single gold table is given along... |
2502.11736 | ReviewEval: An Evaluation Framework for AI-Generated Reviews | cs.CL cs.AI | The escalating volume of academic research, coupled with a shortage of
qualified reviewers, necessitates innovative approaches to peer review. While
large language model (LLMs) offer potential for automating this process, their
current limitations include superficial critiques, hallucinations, and a lack
of actionabl... |
2502.11740 | Mitigating Visual Knowledge Forgetting in MLLM Instruction-tuning via
Modality-decoupled Gradient Descent | cs.LG cs.CV | Recent MLLMs have shown emerging visual understanding and reasoning abilities
after being pre-trained on large-scale multimodal datasets. Unlike
pre-training, where MLLMs receive rich visual-text alignment,
instruction-tuning is often text-driven with weaker visual supervision, leading
to the degradation of pre-train... |
2502.11741 | SQL-o1: A Self-Reward Heuristic Dynamic Search Method for Text-to-SQL | cs.DB cs.AI | The Text-to-SQL(Text2SQL) task aims to convert natural language queries into
executable SQL queries. Thanks to the application of large language models
(LLMs), significant progress has been made in this field. However, challenges
such as model scalability, limited generation space, and coherence issues in
SQL generat... |
2502.11742 | Range and Bird's Eye View Fused Cross-Modal Visual Place Recognition | cs.CV | Image-to-point cloud cross-modal Visual Place Recognition (VPR) is a
challenging task where the query is an RGB image, and the database samples are
LiDAR point clouds. Compared to single-modal VPR, this approach benefits from
the widespread availability of RGB cameras and the robustness of point clouds
in providing a... |
2502.11743 | Robust Partial-Label Learning by Leveraging Class Activation Values | cs.LG stat.ML | Real-world training data is often noisy; for example, human annotators assign
conflicting class labels to the same instances. Partial-label learning (PLL) is
a weakly supervised learning paradigm that allows training classifiers in this
context without manual data cleaning. While state-of-the-art methods have good
pr... |
2502.11744 | FUNCTO: Function-Centric One-Shot Imitation Learning for Tool
Manipulation | cs.RO cs.CV | Learning tool use from a single human demonstration video offers a highly
intuitive and efficient approach to robot teaching. While humans can
effortlessly generalize a demonstrated tool manipulation skill to diverse tools
that support the same function (e.g., pouring with a mug versus a teapot),
current one-shot imi... |
2502.11747 | Open-Ended and Knowledge-Intensive Video Question Answering | cs.IR | Video question answering that requires external knowledge beyond the visual
content remains a significant challenge in AI systems. While models can
effectively answer questions based on direct visual observations, they often
falter when faced with questions requiring broader contextual knowledge. To
address this limi... |
2502.11748 | ILIAS: Instance-Level Image retrieval At Scale | cs.CV | This work introduces ILIAS, a new test dataset for Instance-Level Image
retrieval At Scale. It is designed to evaluate the ability of current and
future foundation models and retrieval techniques to recognize particular
objects. The key benefits over existing datasets include large scale, domain
diversity, accurate g... |
2502.11749 | JotlasNet: Joint Tensor Low-Rank and Attention-based Sparse Unrolling
Network for Accelerating Dynamic MRI | cs.CV cs.AI | Joint low-rank and sparse unrolling networks have shown superior performance
in dynamic MRI reconstruction. However, existing works mainly utilized matrix
low-rank priors, neglecting the tensor characteristics of dynamic MRI images,
and only a global threshold is applied for the sparse constraint to the
multi-channel... |
2502.11751 | Language Models Can See Better: Visual Contrastive Decoding For LLM
Multimodal Reasoning | cs.CV cs.AI | Although Large Language Models (LLMs) excel in reasoning and generation for
language tasks, they are not specifically designed for multimodal challenges.
Training Multimodal Large Language Models (MLLMs), however, is
resource-intensive and constrained by various training limitations. In this
paper, we propose the Mod... |
2502.11752 | Early Detection of Human Handover Intentions in Human-Robot
Collaboration: Comparing EEG, Gaze, and Hand Motion | cs.RO cs.HC | Human-robot collaboration (HRC) relies on accurate and timely recognition of
human intentions to ensure seamless interactions. Among common HRC tasks,
human-to-robot object handovers have been studied extensively for planning the
robot's actions during object reception, assuming the human intention for
object handove... |
2502.11753 | HintsOfTruth: A Multimodal Checkworthiness Detection Dataset with Real
and Synthetic Claims | cs.AI | Misinformation can be countered with fact-checking, but the process is costly
and slow. Identifying checkworthy claims is the first step, where automation
can help scale fact-checkers' efforts. However, detection methods struggle with
content that is 1) multimodal, 2) from diverse domains, and 3) synthetic. We
introd... |
2502.11756 | On the Computation of the Fisher Information in Continual Learning | cs.LG cs.AI cs.CV stat.ML | One of the most popular methods for continual learning with deep neural
networks is Elastic Weight Consolidation (EWC), which involves computing the
Fisher Information. The exact way in which the Fisher Information is computed
is however rarely described, and multiple different implementations for it can
be found onl... |
2502.11763 | Lightweight Deepfake Detection Based on Multi-Feature Fusion | cs.CV cs.AI | Deepfake technology utilizes deep learning based face manipulation techniques
to seamlessly replace faces in videos creating highly realistic but
artificially generated content. Although this technology has beneficial
applications in media and entertainment misuse of its capabilities may lead to
serious risks includi... |
2502.11766 | Warmup-Distill: Bridge the Distribution Mismatch between Teacher and
Student before Knowledge Distillation | cs.CL | The widespread deployment of Large Language Models (LLMs) is hindered by the
high computational demands, making knowledge distillation (KD) crucial for
developing compact smaller ones. However, the conventional KD methods endure
the distribution mismatch issue between the teacher and student models, leading
to the po... |
2502.11767 | From Selection to Generation: A Survey of LLM-based Active Learning | cs.LG cs.CL | Active Learning (AL) has been a powerful paradigm for improving model
efficiency and performance by selecting the most informative data points for
labeling and training. In recent active learning frameworks, Large Language
Models (LLMs) have been employed not only for selection but also for generating
entirely new da... |
2502.11770 | Cognitive-Aligned Document Selection for Retrieval-augmented Generation | cs.AI | Large language models (LLMs) inherently display hallucinations since the
precision of generated texts cannot be guaranteed purely by the parametric
knowledge they include. Although retrieval-augmented generation (RAG) systems
enhance the accuracy and reliability of generative models by incorporating
external document... |
2502.11771 | The Validation Gap: A Mechanistic Analysis of How Language Models
Compute Arithmetic but Fail to Validate It | cs.CL cs.AI | The ability of large language models (LLMs) to validate their output and
identify potential errors is crucial for ensuring robustness and reliability.
However, current research indicates that LLMs struggle with self-correction,
encountering significant challenges in detecting errors. While studies have
explored metho... |
2502.11774 | Interpretable Machine Learning for Kronecker Coefficients | cs.LG math.CO math.RT stat.ML | We analyze the saliency of neural networks and employ interpretable machine
learning models to predict whether the Kronecker coefficients of the symmetric
group are zero or not. Our models use triples of partitions as input features,
as well as b-loadings derived from the principal component of an embedding that
capt... |
2502.11775 | video-SALMONN-o1: Reasoning-enhanced Audio-visual Large Language Model | cs.CV | While recent advancements in reasoning optimization have significantly
enhanced the capabilities of large language models (LLMs), existing efforts to
improve reasoning have been limited to solving mathematical problems and
focusing on visual graphical inputs, neglecting broader applications in general
video understan... |
2502.11777 | Deep Neural Networks for Accurate Depth Estimation with Latent Space
Features | cs.CV cs.AI | Depth estimation plays a pivotal role in advancing human-robot interactions,
especially in indoor environments where accurate 3D scene reconstruction is
essential for tasks like navigation and object handling. Monocular depth
estimation, which relies on a single RGB camera, offers a more affordable
solution compared ... |
2502.11778 | Private Synthetic Graph Generation and Fused Gromov-Wasserstein Distance | stat.ML cs.DS cs.LG math.PR | Networks are popular for representing complex data. In particular,
differentially private synthetic networks are much in demand for method and
algorithm development. The network generator should be easy to implement and
should come with theoretical guarantees. Here we start with complex data as
input and jointly prov... |
2502.11779 | Efficient Response Generation Method Selection for Fine-Tuning Large
Language Models | cs.CL | The training data for fine-tuning large language models (LLMs) is typically
structured as input-output pairs. However, for many tasks, there can be
multiple equally valid output variations for the same input. Recent studies
have observed that the choice of output variation used in training can affect
the model's perf... |
2502.11785 | Changing the Rules of the Game: Reasoning about Dynamic Phenomena in
Multi-Agent Systems | cs.LO cs.MA | The design and application of multi-agent systems (MAS) require reasoning
about the effects of modifications on their underlying structure. In
particular, such changes may impact the satisfaction of system specifications
and the strategic abilities of their autonomous components. In this paper, we
are concerned with ... |
2502.11789 | Personality Editing for Language Models through Relevant Knowledge
Editing | cs.CL | Large Language Models (LLMs) play a vital role in applications like
conversational agents and content creation, where controlling a model's
personality is crucial for maintaining tone, consistency, and engagement.
However, traditional prompt-based techniques for controlling personality often
fall short, as they do no... |
2502.11799 | Table-Critic: A Multi-Agent Framework for Collaborative Criticism and
Refinement in Table Reasoning | cs.AI cs.CL | Despite the remarkable capabilities of large language models (LLMs) in
various reasoning tasks, they still struggle with table reasoning tasks,
particularly in maintaining consistency throughout multi-step reasoning
processes. While existing approaches have explored various decomposition
strategies, they often lack e... |
2502.11800 | Residual Learning towards High-fidelity Vehicle Dynamics Modeling with
Transformer | cs.RO | The vehicle dynamics model serves as a vital component of autonomous driving
systems, as it describes the temporal changes in vehicle state. In a long
period, researchers have made significant endeavors to accurately model vehicle
dynamics. Traditional physics-based methods employ mathematical formulae to
model vehic... |
2502.11801 | 3D Gaussian Inpainting with Depth-Guided Cross-View Consistency | cs.CV cs.LG | When performing 3D inpainting using novel-view rendering methods like Neural
Radiance Field (NeRF) or 3D Gaussian Splatting (3DGS), how to achieve texture
and geometry consistency across camera views has been a challenge. In this
paper, we propose a framework of 3D Gaussian Inpainting with Depth-Guided
Cross-View Con... |
2502.11806 | Exploring Translation Mechanism of Large Language Models | cs.CL | Large language models (LLMs) have succeeded remarkably in multilingual
translation tasks. However, the inherent translation mechanisms of LLMs remain
poorly understood, largely due to sophisticated architectures and vast
parameter scales. In response to this issue, this study explores the
translation mechanism of LLM... |
2502.11809 | Revealing Bias Formation in Deep Neural Networks Through the Geometric
Mechanisms of Human Visual Decoupling | cs.CV cs.AI | Deep neural networks (DNNs) often exhibit biases toward certain categories
during object recognition, even under balanced training data conditions. The
intrinsic mechanisms underlying these biases remain unclear. Inspired by the
human visual system, which decouples object manifolds through hierarchical
processing to ... |
2502.11811 | FineFilter: A Fine-grained Noise Filtering Mechanism for
Retrieval-Augmented Large Language Models | cs.CL | Retrieved documents containing noise will hinder Retrieval-Augmented
Generation (RAG) from detecting answer clues, necessitating noise filtering
mechanisms to enhance accuracy. Existing methods use re-ranking or
summarization to identify the most relevant sentences, but directly and
accurately locating answer clues f... |
2502.11812 | Towards Understanding Fine-Tuning Mechanisms of LLMs via Circuit
Analysis | cs.CL cs.AI cs.LG | Fine-tuning significantly improves the performance of Large Language Models
(LLMs), yet its underlying mechanisms remain poorly understood. This paper aims
to provide an in-depth interpretation of the fine-tuning process through
circuit analysis, a popular tool in Mechanistic Interpretability (MI). Unlike
previous st... |
2502.11816 | IMTS-Mixer: Mixer-Networks for Irregular Multivariate Time Series
Forecasting | cs.LG | Forecasting Irregular Multivariate Time Series (IMTS) has recently emerged as
a distinct research field, necessitating specialized models to address its
unique challenges. While most forecasting literature assumes regularly spaced
observations without missing values, many real-world datasets - particularly in
healthc... |
2502.11817 | AAKT: Enhancing Knowledge Tracing with Alternate Autoregressive Modeling | cs.AI cs.CY cs.LG | Knowledge Tracing (KT) aims to predict students' future performances based on
their former exercises and additional information in educational settings. KT
has received significant attention since it facilitates personalized
experiences in educational situations. Simultaneously, the autoregressive
modeling on the seq... |
2502.11821 | Unitary orthonormal bases of finite dimensional inclusions | math.OA cs.IT math-ph math.IT math.MP quant-ph | We study unitary orthonormal bases in the sense of Pimsner and Popa for
inclusions $(\mathcal{B}\subseteq \mathcal{A}, E),$ where $\mathcal{A},
\mathcal{B}$ are finite dimensional von Neumann algebras and $E$ is a
conditional expectation map from $\mathcal{A}$ onto $\mathcal{B}$. It is shown
that existence of such ba... |
2502.11824 | M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysis | cs.CL | Aspect-based sentiment analysis (ABSA) is a crucial task in information
extraction and sentiment analysis, aiming to identify aspects with associated
sentiment elements in text. However, existing ABSA datasets are predominantly
English-centric, limiting the scope for multilingual evaluation and research.
To bridge th... |
2502.11827 | Influence Operations in Social Networks | cs.SI cs.CY | An important part of online activities are intended to control the public
opinion and behavior, being considered currently a global threat. This article
identifies and conceptualizes seven online strategies employed in social media
influence operations. These procedures are quantified through the analysis of
80 incid... |
2502.11828 | Intersectional Fairness in Reinforcement Learning with Large State and
Constraint Spaces | cs.LG cs.GT | In traditional reinforcement learning (RL), the learner aims to solve a
single objective optimization problem: find the policy that maximizes expected
reward. However, in many real-world settings, it is important to optimize over
multiple objectives simultaneously. For example, when we are interested in
fairness, sta... |
2502.11829 | Code-Vision: Evaluating Multimodal LLMs Logic Understanding and Code
Generation Capabilities | cs.CL cs.AI cs.SE | This paper introduces Code-Vision, a benchmark designed to evaluate the
logical understanding and code generation capabilities of Multimodal Large
Language Models (MLLMs). It challenges MLLMs to generate a correct program that
fulfills specific functionality requirements based on a given flowchart, which
visually rep... |
2502.11830 | Text Classification in the LLM Era -- Where do we stand? | cs.CL | Large Language Models revolutionized NLP and showed dramatic performance
improvements across several tasks. In this paper, we investigated the role of
such language models in text classification and how they compare with other
approaches relying on smaller pre-trained language models. Considering 32
datasets spanning... |
2502.11831 | Intuitive physics understanding emerges from self-supervised pretraining
on natural videos | cs.CV cs.AI | We investigate the emergence of intuitive physics understanding in
general-purpose deep neural network models trained to predict masked regions in
natural videos. Leveraging the violation-of-expectation framework, we find that
video prediction models trained to predict outcomes in a learned representation
space demon... |
2502.11835 | Neural Chaos: A Spectral Stochastic Neural Operator | cs.CE physics.comp-ph stat.ML | Building surrogate models with uncertainty quantification capabilities is
essential for many engineering applications where randomness, such as
variability in material properties, is unavoidable. Polynomial Chaos Expansion
(PCE) is widely recognized as a to-go method for constructing stochastic
solutions in both intr... |
2502.11836 | Model Generalization on Text Attribute Graphs: Principles with Large
Language Models | cs.LG | Large language models (LLMs) have recently been introduced to graph learning,
aiming to extend their zero-shot generalization success to tasks where labeled
graph data is scarce. Among these applications, inference over text-attributed
graphs (TAGs) presents unique challenges: existing methods struggle with LLMs'
lim... |
2502.11840 | ChordFormer: A Conformer-Based Architecture for Large-Vocabulary Audio
Chord Recognition | cs.SD cs.AI cs.CV cs.IR cs.LG | Chord recognition serves as a critical task in music information retrieval
due to the abstract and descriptive nature of chords in music analysis. While
audio chord recognition systems have achieved significant accuracy for small
vocabularies (e.g., major/minor chords), large-vocabulary chord recognition
remains a ch... |
2502.11843 | Can LLM Agents Maintain a Persona in Discourse? | cs.CL cs.AI cs.SI | Large Language Models (LLMs) are widely used as conversational agents,
exploiting their capabilities in various sectors such as education, law,
medicine, and more. However, LLMs are often subjected to context-shifting
behaviour, resulting in a lack of consistent and interpretable
personality-aligned interactions. Adh... |
2502.11844 | BaxBench: Can LLMs Generate Correct and Secure Backends? | cs.CR cs.AI cs.LG cs.PL | The automatic generation of programs has long been a fundamental challenge in
computer science. Recent benchmarks have shown that large language models
(LLMs) can effectively generate code at the function level, make code edits,
and solve algorithmic coding tasks. However, to achieve full automation, LLMs
should be a... |
2502.11850 | Steering the LoCoMotif: Using Domain Knowledge in Time Series Motif
Discovery | cs.LG cs.AI cs.CV | Time Series Motif Discovery (TSMD) identifies repeating patterns in time
series data, but its unsupervised nature might result in motifs that are not
interesting to the user. To address this, we propose a framework that allows
the user to impose constraints on the motifs to be discovered, where
constraints can easily... |
2502.11853 | StructTransform: A Scalable Attack Surface for Safety-Aligned Large
Language Models | cs.LG | In this work, we present a series of structure transformation attacks on LLM
alignment, where we encode natural language intent using diverse syntax spaces,
ranging from simple structure formats and basic query languages (e.g. SQL) to
new novel spaces and syntaxes created entirely by LLMs. Our extensive
evaluation sh... |
2502.11854 | Enhanced Anomaly Detection in IoMT Networks using Ensemble AI Models on
the CICIoMT2024 Dataset | cs.CR cs.LG | The rapid proliferation of Internet of Medical Things (IoMT) devices in
healthcare has introduced unique cybersecurity challenges, primarily due to the
diverse communication protocols and critical nature of these devices This
research aims to develop an advanced, real-time anomaly detection framework
tailored for IoM... |
2502.11856 | LLMs as a synthesis between symbolic and continuous approaches to
language | cs.CL | Since the middle of the 20th century, a fierce battle is being fought between
symbolic and continuous approaches to language and cognition. The success of
deep learning models, and LLMs in particular, has been alternatively taken as
showing that the continuous camp has won, or dismissed as an irrelevant
engineering d... |
2502.11858 | Rethinking Audio-Visual Adversarial Vulnerability from Temporal and
Modality Perspectives | cs.SD cs.CV | While audio-visual learning equips models with a richer understanding of the
real world by leveraging multiple sensory modalities, this integration also
introduces new vulnerabilities to adversarial attacks.
In this paper, we present a comprehensive study of the adversarial robustness
of audio-visual models, consid... |
2502.11859 | Defining and Evaluating Visual Language Models' Basic Spatial Abilities:
A Perspective from Psychometrics | cs.CV cs.CL | The Theory of Multiple Intelligences underscores the hierarchical nature of
cognitive capabilities. To advance Spatial Artificial Intelligence, we pioneer
a psychometric framework defining five Basic Spatial Abilities (BSAs) in Visual
Language Models (VLMs): Spatial Perception, Spatial Relation, Spatial
Orientation, ... |
2502.11861 | Exploring Large Language Models in Healthcare: Insights into Corpora
Sources, Customization Strategies, and Evaluation Metrics | cs.CL | This study reviewed the use of Large Language Models (LLMs) in healthcare,
focusing on their training corpora, customization techniques, and evaluation
metrics. A systematic search of studies from 2021 to 2024 identified 61
articles. Four types of corpora were used: clinical resources, literature,
open-source dataset... |
2502.11862 | Understanding In-Context Machine Translation for Low-Resource Languages:
A Case Study on Manchu | cs.CL | In-context machine translation (MT) with large language models (LLMs) is a
promising approach for low-resource MT, as it can readily take advantage of
linguistic resources such as grammar books and dictionaries. Such resources are
usually selectively integrated into the prompt so that LLMs can directly
perform transl... |
2502.11863 | FedEAT: A Robustness Optimization Framework for Federated LLMs | cs.LG cs.AI | Significant advancements have been made by Large Language Models (LLMs) in
the domains of natural language understanding and automated content creation.
However, they still face persistent problems, including substantial
computational costs and inadequate availability of training data. The
combination of Federated Le... |
2502.11864 | Does Knowledge About Perceptual Uncertainty Help an Agent in Automated
Driving? | cs.CV cs.RO | Agents in real-world scenarios like automated driving deal with uncertainty
in their environment, in particular due to perceptual uncertainty. Although,
reinforcement learning is dedicated to autonomous decision-making under
uncertainty these algorithms are typically not informed about the uncertainty
currently conta... |
2502.11866 | Southern Newswire Corpus: A Large-Scale Dataset of Mid-Century Wire
Articles Beyond the Front Page | cs.CL | I introduce a new large-scale dataset of historical wire articles from U.S.
Southern newspapers, spanning 1960-1975 and covering multiple wire services:
The Associated Press, United Press International, Newspaper Enterprise
Association. Unlike prior work focusing on front-page content, this dataset
captures articles ... |
2502.11867 | On Data-Driven Robust Optimization With Multiple Uncertainty Subsets:
Unified Uncertainty Set Representation and Mitigating Conservatism | math.OC cs.SY eess.SY | Constructing uncertainty sets as unions of multiple subsets has emerged as an
effective approach for creating compact and flexible uncertainty
representations in data-driven robust optimization (RO). This paper focuses on
two separate research questions. The first concerns the computational challenge
in applying thes... |
2502.11874 | VAQUUM: Are Vague Quantifiers Grounded in Visual Data? | cs.CL | Vague quantifiers such as "a few" and "many" are influenced by many
contextual factors, including how many objects are present in a given context.
In this work, we evaluate the extent to which vision-and-language models (VLMs)
are compatible with humans when producing or judging the appropriateness of
vague quantifie... |
2502.11877 | JoLT: Joint Probabilistic Predictions on Tabular Data Using LLMs | stat.ML cs.LG | We introduce a simple method for probabilistic predictions on tabular data
based on Large Language Models (LLMs) called JoLT (Joint LLM Process for
Tabular data). JoLT uses the in-context learning capabilities of LLMs to define
joint distributions over tabular data conditioned on user-specified side
information about... |
2502.11880 | Bitnet.cpp: Efficient Edge Inference for Ternary LLMs | cs.LG cs.AI cs.CL cs.DC | The advent of 1-bit large language models (LLMs), led by BitNet b1.58, has
spurred interest in ternary LLMs. Despite this, research and practical
applications focusing on efficient edge inference for ternary LLMs remain
scarce. To bridge this gap, we introduce Bitnet.cpp, an inference system
optimized for BitNet b1.5... |
2502.11881 | Hypothesis-Driven Theory-of-Mind Reasoning for Large Language Models | cs.AI cs.CL | Existing LLM reasoning methods have shown impressive capabilities across
various tasks, such as solving math and coding problems. However, applying
these methods to scenarios without ground-truth answers or rule-based
verification methods - such as tracking the mental states of an agent - remains
challenging. Inspire... |
2502.11882 | Leveraging Dual Process Theory in Language Agent Framework for Real-time
Simultaneous Human-AI Collaboration | cs.AI cs.CL cs.HC cs.LG cs.MA | Agents built on large language models (LLMs) have excelled in turn-by-turn
human-AI collaboration but struggle with simultaneous tasks requiring real-time
interaction. Latency issues and the challenge of inferring variable human
strategies hinder their ability to make autonomous decisions without explicit
instruction... |
2502.11883 | FairDiverse: A Comprehensive Toolkit for Fair and Diverse Information
Retrieval Algorithms | cs.IR | In modern information retrieval (IR). achieving more than just accuracy is
essential to sustaining a healthy ecosystem, especially when addressing
fairness and diversity considerations. To meet these needs, various datasets,
algorithms, and evaluation frameworks have been introduced. However, these
algorithms are oft... |
2502.11886 | LIMR: Less is More for RL Scaling | cs.LG cs.AI cs.CL | In this paper, we ask: what truly determines the effectiveness of RL training
data for enhancing language models' reasoning capabilities? While recent
advances like o1, Deepseek R1, and Kimi1.5 demonstrate RL's potential, the lack
of transparency about training data requirements has hindered systematic
progress. Star... |
2502.11887 | Stonefish: Supporting Machine Learning Research in Marine Robotics | cs.RO cs.AI cs.SY eess.SY | Simulations are highly valuable in marine robotics, offering a cost-effective
and controlled environment for testing in the challenging conditions of
underwater and surface operations. Given the high costs and logistical
difficulties of real-world trials, simulators capable of capturing the
operational conditions of ... |
2502.11890 | Revisiting Classification Taxonomy for Grammatical Errors | cs.CL | Grammatical error classification plays a crucial role in language learning
systems, but existing classification taxonomies often lack rigorous validation,
leading to inconsistencies and unreliable feedback. In this paper, we revisit
previous classification taxonomies for grammatical errors by introducing a
systematic... |
2502.11891 | From Open-Vocabulary to Vocabulary-Free Semantic Segmentation | cs.CV | Open-vocabulary semantic segmentation enables models to identify novel object
categories beyond their training data. While this flexibility represents a
significant advancement, current approaches still rely on manually specified
class names as input, creating an inherent bottleneck in real-world
applications. This w... |
2502.11893 | Rethinking Benign Overfitting in Two-Layer Neural Networks | cs.LG stat.ML | Recent theoretical studies (Kou et al., 2023; Cao et al., 2022) have revealed
a sharp phase transition from benign to harmful overfitting when the
noise-to-feature ratio exceeds a threshold-a situation common in long-tailed
data distributions where atypical data is prevalent. However, harmful
overfitting rarely happe... |
2502.11895 | Continual Quantization-Aware Pre-Training: When to transition from
16-bit to 1.58-bit pre-training for BitNet language models? | cs.LG cs.AI | Large language models (LLMs) require immense resources for training and
inference. Quantization, a technique that reduces the precision of model
parameters, offers a promising solution for improving LLM efficiency and
sustainability. While post-training quantization methods typically achieve 4-8
bits per parameter, r... |
2502.11896 | CAMEL: Continuous Action Masking Enabled by Large Language Models for
Reinforcement Learning | cs.LG cs.AI | Reinforcement learning (RL) in continuous action spaces encounters persistent
challenges, such as inefficient exploration and convergence to suboptimal
solutions. To address these limitations, we propose CAMEL, a novel framework
integrating LLM-generated suboptimal policies into the RL training pipeline.
CAMEL levera... |
2502.11897 | DLFR-VAE: Dynamic Latent Frame Rate VAE for Video Generation | cs.CV cs.AI | In this paper, we propose the Dynamic Latent Frame Rate VAE (DLFR-VAE), a
training-free paradigm that can make use of adaptive temporal compression in
latent space. While existing video generative models apply fixed compression
rates via pretrained VAE, we observe that real-world video content exhibits
substantial te... |
2502.11900 | Ansatz-free Hamiltonian learning with Heisenberg-limited scaling | quant-ph cs.IT cs.LG math.IT | Learning the unknown interactions that govern a quantum system is crucial for
quantum information processing, device benchmarking, and quantum sensing. The
problem, known as Hamiltonian learning, is well understood under the assumption
that interactions are local, but this assumption may not hold for arbitrary
Hamilt... |
2502.11901 | Building A Proof-Oriented Programmer That Is 64% Better Than GPT-4o
Under Data Scarsity | cs.CL cs.PL cs.SE | Existing LMs struggle with proof-oriented programming due to data scarcity,
which manifest in two key ways: (1) a lack of sufficient corpora for
proof-oriented programming languages such as F*, and (2) the absence of
large-scale, project-level proof-oriented implementations that can teach the
model the intricate reas... |
2502.11903 | MMRC: A Large-Scale Benchmark for Understanding Multimodal Large
Language Model in Real-World Conversation | cs.CL | Recent multimodal large language models (MLLMs) have demonstrated significant
potential in open-ended conversation, generating more accurate and personalized
responses. However, their abilities to memorize, recall, and reason in
sustained interactions within real-world scenarios remain underexplored. This
paper intro... |
2502.11904 | A formal implementation of Behavior Trees to act in robotics | cs.RO | Behavior Trees (BT) are becoming quite popular as an Acting component of
autonomous robotic systems. We propose to define a formal semantics to BT by
translating them to a formal language which enables us to perform verification
of programs written with BT, as well as runtime verification while these BT
execute. This... |
2502.11909 | Neural Guided Diffusion Bridges | stat.ML cs.LG | We propose a novel method for simulating conditioned diffusion processes
(diffusion bridges) in Euclidean spaces. By training a neural network to
approximate bridge dynamics, our approach eliminates the need for
computationally intensive Markov Chain Monte Carlo (MCMC) methods or
reverse-process modeling. Compared to... |
2502.11910 | Adversarial Alignment for LLMs Requires Simpler, Reproducible, and More
Measurable Objectives | cs.LG | Misaligned research objectives have considerably hindered progress in
adversarial robustness research over the past decade. For instance, an
extensive focus on optimizing target metrics, while neglecting rigorous
standardized evaluation, has led researchers to pursue ad-hoc heuristic
defenses that were seemingly effe... |
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