Daily Arxiv

전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
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논문에 대한 저작권은 저자 및 해당 기관에 있으며, 공유 시 출처만 명기하면 됩니다.

AutoChemSchematic AI: A Closed-Loop, Physics-Aware Agentic Framework for Auto-Generating Chemical Process and Instrumentation Diagrams

LlamaRL: A Distributed Asynchronous Reinforcement Learning Framework for Efficient Large-scale LLM Training

DLP: Dynamic Layerwise Pruning in Large Language Models

MedHELM: Holistic Evaluation of Large Language Models for Medical Tasks

Estimating LLM Consistency: A User Baseline vs Surrogate Metrics

Mind the Gap: A Practical Attack on GGUF Quantization

Bounded Rationality for LLMs: Satisficing Alignment at Inference-Time

GSO: Challenging Software Optimization Tasks for Evaluating SWE-Agents

Active Layer-Contrastive Decoding Reduces Hallucination in Large Language Model Generation

Jigsaw-R1: A Study of Rule-based Visual Reinforcement Learning with Jigsaw Puzzles

Cognitive Guardrails for Open-World Decision Making in Autonomous Drone Swarms

SWE-bench Goes Live!

Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization

Matryoshka Model Learning for Improved Elastic Student Models

Context-Robust Knowledge Editing for Language Models

HiLDe: Intentional Code Generation via Human-in-the-Loop Decoding

FAMA: The First Large-Scale Open-Science Speech Foundation Model for English and Italian

FastTD3: Simple, Fast, and Capable Reinforcement Learning for Humanoid Control

Judging Quality Across Languages: A Multilingual Approach to Pretraining Data Filtering with Language Models

iDSE: Navigating Design Space Exploration in High-Level Synthesis Using LLMs

Practical Adversarial Attacks on Stochastic Bandits via Fake Data Injection

RePaViT: Scalable Vision Transformer Acceleration via Structural Reparameterization on Feedforward Network Layers

More Thinking, Less Seeing? Assessing Amplified Hallucination in Multimodal Reasoning Models

Hume: Introducing System-2 Thinking in Visual-Language-Action Model

Universal Value-Function Uncertainties

How Do Transformers Learn Variable Binding in Symbolic Programs?

Adversarial bandit optimization for approximately linear functions

Hierarchical Retrieval with Evidence Curation for Open-Domain Financial Question Answering on Standardized Documents

Homophily Enhanced Graph Domain Adaptation

NeuSym-RAG: Hybrid Neural Symbolic Retrieval with Multiview Structuring for PDF Question Answering

SoloSpeech: Enhancing Intelligibility and Quality in Target Speech Extraction through a Cascaded Generative Pipeline

An Interpretable Representation Learning Approach for Diffusion Tensor Imaging

InfoChartQA: A Benchmark for Multimodal Question Answering on Infographic Charts

Moderating Harm: Benchmarking Large Language Models for Cyberbullying Detection in YouTube Comments

Security Concerns for Large Language Models: A Survey

A Survey of LLM $\times$ DATA

Task-Optimized Convolutional Recurrent Networks Align with Tactile Processing in the Rodent Brain

NMCSE: Noise-Robust Multi-Modal Coupling Signal Estimation Method via Optimal Transport for Cardiovascular Disease Detection

FRIREN: Beyond Trajectories -- A Spectral Lens on Time

A Fully Generative Motivational Interviewing Counsellor Chatbot for Moving Smokers Towards the Decision to Quit

TrimR: Verifier-based Training-Free Thinking Compression for Efficient Test-Time Scaling

Beyond Face Swapping: A Diffusion-Based Digital Human Benchmark for Multimodal Deepfake Detection

Attributing Response to Context: A Jensen-Shannon Divergence Driven Mechanistic Study of Context Attribution in Retrieval-Augmented Generation

QuickVideo: Real-Time Long Video Understanding with System Algorithm Co-Design

Replay Attacks Against Audio Deepfake Detection

Forensic deepfake audio detection using segmental speech features

A3 : an Analytical Low-Rank Approximation Framework for Attention

A Survey of 3D Reconstruction with Event Cameras

Confabulation dynamics in a reservoir computer: Filling in the gaps with untrained attractors

WATCH: Adaptive Monitoring for AI Deployments via Weighted-Conformal Martingales

Deep Learning Framework for Infrastructure Maintenance: Crack Detection and High-Resolution Imaging of Infrastructure Surfaces

GAME: Learning Multimodal Interactions via Graph Structures for Personality Trait Estimation

LENSLLM: Unveiling Fine-Tuning Dynamics for LLM Selection

Motion-compensated cardiac MRI using low-rank diffeomorphic flow (DMoCo)

OODTE: A Differential Testing Engine for the ONNX Optimizer

Handling Label Noise via Instance-Level Difficulty Modeling and Dynamic Optimization

Prisma: An Open Source Toolkit for Mechanistic Interpretability in Vision and Video

Depth-Constrained ASV Navigation with Deep RL and Limited Sensing

(Im)possibility of Automated Hallucination Detection in Large Language Models

Unveiling the Lack of LVLM Robustness to Fundamental Visual Variations: Why and Path Forward

Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research

The Hitchhiker's Guide to Program Analysis, Part II: Deep Thoughts by LLMs

The Structural Safety Generalization Problem

Parameterized Synthetic Text Generation with SimpleStories

SD$^2$: Self-Distilled Sparse Drafters

Persona Dynamics: Unveiling the Impact of Personality Traits on Agents in Text-Based Games

Semantic-guided Representation Learning for Multi-Label Recognition

SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge Refinement

Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translation

A Conformal Risk Control Framework for Granular Word Assessment and Uncertainty Calibration of CLIPScore Quality Estimates

Understanding Inequality of LLM Fact-Checking over Geographic Regions with Agent and Retrieval models

ADS-Edit: A Multimodal Knowledge Editing Dataset for Autonomous Driving Systems

A Survey on Event-driven 3D Reconstruction: Development under Different Categories

VecTrans: Enhancing Compiler Auto-Vectorization through LLM-Assisted Code Transformations

REALM: A Dataset of Real-World LLM Use Cases

Opportunities and Challenges of Frontier Data Governance With Synthetic Data

ARFlow: Human Action-Reaction Flow Matching with Physical Guidance

Position: Beyond Assistance - Reimagining LLMs as Ethical and Adaptive Co-Creators in Mental Health Care

Redefining Toxicity: An Objective and Context-Aware Approach for Stress-Level-Based Detection

A Dual-Directional Context-Aware Test-Time Learning for Text Classification

MentalChat16K: A Benchmark Dataset for Conversational Mental Health Assistance

ClusComp: A Simple Paradigm for Model Compression and Efficient Finetuning

FMNet: Frequency-Assisted Mamba-Like Linear Attention Network for Camouflaged Object Detection

NFIG: Autoregressive Image Generation with Next-Frequency Prediction

GMLM: Bridging Graph Neural Networks and Language Models for Heterophilic Node Classification

CSTRL: Context-Driven Sequential Transfer Learning for Abstractive Radiology Report Summarization

Wanda++: Pruning Large Language Models via Regional Gradients

HoH: A Dynamic Benchmark for Evaluating the Impact of Outdated Information on Retrieval-Augmented Generation

Optimizing Multi-Hop Document Retrieval Through Intermediate Representations

Causally Reliable Concept Bottleneck Models

SafeVLA: Towards Safety Alignment of Vision-Language-Action Model via Constrained Learning

A Comprehensive Survey of Machine Unlearning Techniques for Large Language Models

Marco-o1 v2: Towards Widening The Distillation Bottleneck for Reasoning Models

POPGym Arcade: Parallel Pixelated POMDPs

Semantic Integrity Constraints: Declarative Guardrails for AI-Augmented Data Processing Systems

Quantifying First-Order Markov Violations in Noisy Reinforcement Learning: A Causal Discovery Approach

SPD: Sync-Point Drop for Efficient Tensor Parallelism of Large Language Models

Mixture of Structural-and-Textual Retrieval over Text-rich Graph Knowledge Bases

Faithful Logic Embeddings in HOL -- Deep and Shallow

TestNUC: Enhancing Test-Time Computing Approaches and Scaling through Neighboring Unlabeled Data Consistency

Image Captioning Evaluation in the Age of Multimodal LLMs: Challenges and Future Perspectives

Created by

Haebom

저자

Sara Sarto, Marcella Cornia, Rita Cucchiara

개요

본 논문은 기계가 생성한 이미지 캡션 평가의 발전에 대한 종합적인 개요를 제공합니다. 다중 모달 대규모 언어 모델(MLLM)의 등장으로 이미지 캡션 생성이 핵심 과제가 됨에 따라 강력하고 신뢰할 수 있는 평가 지표에 대한 필요성이 증가했습니다. 본 논문은 기존 지표의 진화, 강점 및 한계를 분석하며, 인간 판단과의 상관관계, 순위 정확도 및 환각에 대한 민감도를 포함한 여러 차원에서 이러한 지표를 평가합니다. 또한, MLLM이 생성하는 더 길고 자세한 캡션이 제기하는 과제를 탐구하고 현재 지표의 이러한 스타일 변화에 대한 적응성을 검토합니다. 표준 평가 방법의 몇 가지 한계를 강조하고 이미지 캡션 평가에 대한 미래 연구를 위한 유망한 방향을 제시합니다.

시사점, 한계점

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시사점:

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기존 이미지 캡션 평가 지표의 한계를 명확히 제시하고, 향후 연구 방향을 제시함으로써 더욱 정교하고 신뢰할 수 있는 평가 방법 개발을 촉진합니다.

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MLLM이 생성하는 더 길고 복잡한 캡션에 대한 평가 지표의 적응성 문제를 다룸으로써, 실제 응용에 더욱 적합한 평가 체계 구축에 기여합니다.

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인간 판단과의 상관관계, 순위 정확도, 환각에 대한 민감도 등 다양한 측면에서 기존 지표를 분석하여, 평가 지표 선택 및 개발에 대한 통찰력을 제공합니다.

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한계점:

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제시된 미래 연구 방향이 구체적인 방법론이나 실험 설계를 포함하지 않아, 실제 연구로 이어지는 데 어려움이 있을 수 있습니다.

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논문에서 다루는 평가 지표의 종류와 범위가 명시적으로 제시되지 않아, 전체적인 평가의 객관성에 대한 의문이 남을 수 있습니다.

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MLLM의 특성을 고려한 새로운 평가 지표의 구체적인 제안이 부족하여, 실질적인 해결책 제시에는 한계가 있습니다.

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