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.

Ring-lite: Scalable Reasoning via C3PO-Stabilized Reinforcement Learning for LLMs

Accurate and scalable exchange-correlation with deep learning

AIn't Nothing But a Survey? Using Large Language Models for Coding German Open-Ended Survey Responses on Survey Motivation

Probabilistic Aggregation and Targeted Embedding Optimization for Collective Moral Reasoning in Large Language Models

Aligning Evaluation with Clinical Priorities: Calibration, Label Shift, and Error Costs

GRAM: A Generative Foundation Reward Model for Reward Generalization

VideoMAR: Autoregressive Video Generatio with Continuous Tokens

FrontendBench: A Benchmark for Evaluating LLMs on Front-End Development via Automatic Evaluation

Seewo's Submission to MLC-SLM: Lessons learned from Speech Reasoning Language Models

No-Regret Learning Under Adversarial Resource Constraints: A Spending Plan Is All You Need!

Serving Large Language Models on Huawei CloudMatrix384

PLD: A Choice-Theoretic List-Wise Knowledge Distillation

TARDIS STRIDE: A Spatio-Temporal Road Image Dataset and World Model for Autonomy

Refactoring Codebases through Library Design

TransXSSM: A Hybrid Transformer State Space Model with Unified Rotary Position Embedding

Multi-Agent Language Models: Advancing Cooperation, Coordination, and Adaptation

Multi-Task Reward Learning from Human Ratings

Too Big to Think: Capacity, Memorization, and Generalization in Pre-Trained Transformers

Router-R1: Teaching LLMs Multi-Round Routing and Aggregation via Reinforcement Learning

Vision Transformers Don't Need Trained Registers

BIS Reasoning 1.0: The First Large-Scale Japanese Benchmark for Belief-Inconsistent Syllogistic Reasoning

LaMP-Cap: Personalized Figure Caption Generation With Multimodal Figure Profiles

CORA: Coalitional Rational Advantage Decomposition for Multi-Agent Policy Gradients

Supervised Quantum Machine Learning: A Future Outlook from Qubits to Enterprise Applications

ChemHAS: Hierarchical Agent Stacking for Enhancing Chemistry Tools

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models

Think Twice before Adaptation: Improving Adaptability of DeepFake Detection via Online Test-Time Adaptation

Efficient Long CoT Reasoning in Small Language Models

Imagine Beyond! Distributionally Robust Auto-Encoding for State Space Coverage in Online Reinforcement Learning

MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion

J4R: Learning to Judge with Equivalent Initial State Group Relative Policy Optimization

Fractured Chain-of-Thought Reasoning

DreamGen: Unlocking Generalization in Robot Learning through Video World Models

UD-English-CHILDES: A Collected Resource of Gold and Silver Universal Dependencies Trees for Child Language Interactions

Position Paper: Rethinking Privacy in RL for Sequential Decision-making in the Age of LLMs

Influential Bandits: Pulling an Arm May Change the Environment

SCAM: A Real-World Typographic Robustness Evaluation for Multimodal Foundation Models

Trust Region Preference Approximation: A simple and stable reinforcement learning algorithm for LLM reasoning

Exploring Personalized Federated Learning Architectures for Violence Detection in Surveillance Videos

A Bird Song Detector for improving bird identification through Deep Learning: a case study from Do\~nana

KANITE: Kolmogorov-Arnold Networks for ITE estimation

Beyond Propagation of Chaos: A Stochastic Algorithm for Mean Field Optimization

Resolving UnderEdit & OverEdit with Iterative & Neighbor-Assisted Model Editing

Adding Chocolate to Mint: Mitigating Metric Interference in Machine Translation

EgoBlind: Towards Egocentric Visual Assistance for the Blind

PsychBench: A comprehensive and professional benchmark for evaluating the performance of LLM-assisted psychiatric clinical practice

Machine Learners Should Acknowledge the Legal Implications of Large Language Models as Personal Data

Supporting the development of Machine Learning for fundamental science in a federated Cloud with the AI_INFN platform

CODESYNC: Synchronizing Large Language Models with Dynamic Code Evolution at Scale

Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning

Task-Aware Virtual Training: Enhancing Generalization in Meta-Reinforcement Learning for Out-of-Distribution Tasks

Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions

Perspective Transition of Large Language Models for Solving Subjective Tasks

Can LLMs Ask Good Questions?

Aligning AI Research with the Needs of Clinical Coding Workflows: Eight Recommendations Based on US Data Analysis and Critical Review

SurgSora: Object-Aware Diffusion Model for Controllable Surgical Video Generation

Large Language Models for Automated Literature Review: An Evaluation of Reference Generation, Abstract Writing, and Review Composition

Multiclass Post-Earthquake Building Assessment Integrating High-Resolution Optical and SAR Satellite Imagery, Ground Motion, and Soil Data with Transformers

REVOLVE: Optimizing AI Systems by Tracking Response Evolution in Textual Optimization

FLARE: Towards Universal Dataset Purification against Backdoor Attacks

Heterogeneous Relationships of Subjects and Shapelets for Semi-supervised Multivariate Series Classification

Contrast Similarity-Aware Dual-Pathway Mamba for Multivariate Time Series Node Classification

Semantic-Geometric-Physical-Driven Robot Manipulation Skill Transfer via Skill Library and Tactile Representation

LL\"aMmlein: Transparent, Compact and Competitive German-Only Language Models from Scratch

Math Neurosurgery: Isolating Language Models' Math Reasoning Abilities Using Only Forward Passes

The Epochal Sawtooth Phenomenon: Unveiling Training Loss Oscillations in Adam and Other Optimizers

Pap2Pat: Benchmarking Outline-Guided Long-Text Patent Generation with Patent-Paper Pairs

Deep Graph Anomaly Detection: A Survey and New Perspectives

A Novel Perturb-ability Score to Mitigate Evasion Adversarial Attacks on Flow-Based ML-NIDS

Style-Preserving Lip Sync via Audio-Aware Style Reference

Advancing oncology with federated learning: transcending boundaries in breast, lung, and prostate cancer. A systematic review

Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey

Informed Correctors for Discrete Diffusion Models

RadioRAG: Online Retrieval-augmented Generation for Radiology Question Answering

A Systematic Survey of Natural Language Processing for the Greek Language

Predicting the Understandability of Computational Notebooks through Code Metrics Analysis

An Effective Incorporating Heterogeneous Knowledge Curriculum Learning for Sequence Labeling

HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction

The NordDRG AI Benchmark for Large Language Models

From Data-Driven to Purpose-Driven Artificial Intelligence: Systems Thinking for Data-Analytic Automation of Patient Care

Breaking Bad Molecules: Are MLLMs Ready for Structure-Level Molecular Detoxification?

Entropy-based Exploration Conduction for Multi-step Reasoning

Solving Satisfiability Modulo Counting Exactly with Probabilistic Circuits

Synthesizing Composite Hierarchical Structure from Symbolic Music Corpora

Learning Strategic Language Agents in the Werewolf Game with Iterative Latent Space Policy Optimization

Optimal Transport for Probabilistic Circuits

OM4OV: Leveraging Ontology Matching for Ontology Versioning

Behaviour Planning: A Toolkit for Diverse Planning

Spatial Context-based Self-Supervised Learning for Handwritten Text Recognition

"Generate" the Future of Work through AI: Empirical Evidence from Online Labor Markets

Dense SAE Latents Are Features, Not Bugs

Sekai: A Video Dataset towards World Exploration

Leaky Thoughts: Large Reasoning Models Are Not Private Thinkers

AutoRule: Reasoning Chain-of-thought Extracted Rule-based Rewards Improve Preference Learning

Demystifying the Visual Quality Paradox in Multimodal Large Language Models

Revisiting Compositional Generalization Capability of Large Language Models Considering Instruction Following Ability

Federated Learning for MRI-based BrainAGE: a multicenter study on post-stroke functional outcome prediction

GFLC: Graph-based Fairness-aware Label Correction for Fair Classification

The Compositional Architecture of Regret in Large Language Models

LoX: Low-Rank Extrapolation Robustifies LLM Safety Against Fine-tuning

Adding Chocolate to Mint: Mitigating Metric Interference in Machine Translation

Created by

Haebom

저자

Jose Pombal, Nuno M. Guerreiro, Ricardo Rei, Andre F. T. Martins

개요

자동 평가 지표의 사용이 증가함에 따라 모델 개발 과정에서 의도치 않게 지표를 조작하는 위험(Metric Interference, MINT)이 증가하고 있다. MINT는 모델 조정과 평가에 동일하거나 관련된 지표를 사용하는 것을 의미하며, 시스템 출력이 간섭 지표의 함수가 되어 인간의 판단과의 상관관계가 떨어져 시스템 성능에 대한 과도한 낙관주의를 초래할 수 있다. 본 논문에서는 기계 번역 관련 작업에서 데이터 필터링 및 품질 신호를 사용한 디코딩이라는 두 가지 일반적인 MINT 사례를 분석한다. MINT는 지표가 직접 최적화되지 않더라도 인스턴스 수준 지표 점수를 크게 왜곡한다는 것을 발견했다. 이 문제를 해결하기 위해 MINT 하에서 더욱 신뢰할 수 있는 평가를 위한 MINTADJUST 방법을 제안한다. WMT24 MT 공유 작업 테스트 세트에서 MINTADJUST는 대부분의 언어 쌍에서, 특히 고품질 시스템에 대해 최첨단 지표보다 더 정확하게 번역과 시스템을 순위 지정한다. 또한 MINTADJUST는 주최측이 사용하는 앙상블 방법인 AUTORANK보다 성능이 우수하다.

시사점, 한계점

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시사점:

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MINT 문제의 심각성을 밝히고, 기계 번역 분야에서의 구체적인 사례를 제시함으로써, 자동 평가 지표 사용의 위험성에 대한 경각심을 높였다.

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MINTADJUST라는 새로운 평가 방법을 제안하여 MINT 문제를 완화하고 더욱 신뢰할 수 있는 시스템 평가를 가능하게 하였다.

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WMT24 MT 공유 작업에서 기존 방법들보다 우수한 성능을 보여 MINTADJUST의 실효성을 입증하였다.

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한계점:

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MINTADJUST의 성능이 모든 언어 쌍과 모든 상황에서 우수한 것은 아니다.

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MINTADJUST의 일반화 가능성에 대한 추가적인 연구가 필요하다.

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다른 머신러닝 작업에 대한 MINTADJUST의 적용 가능성 및 일반화 성능에 대한 추가적인 연구가 필요하다.

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