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世界中で発行される人工知能関連の論文をまとめるページです。
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AC-DiT: Adaptive Coordination Diffusion Transformer for Mobile Manipulation

Self-Guided Process Reward Optimization with Redefined Step-wise Advantage for Process Reinforcement Learning

Crafting Hanzi as Narrative Bridges: An AI Co-Creation Workshop for Elderly Migrants

Distributional Soft Actor-Critic with Diffusion Policy

Skywork-Reward-V2: Scaling Preference Data Curation via Human-AI Synergy

Fast AI Model Splitting over Edge Networks

From Sentences to Sequences: Rethinking Languages in Biological System

MTCNet: Motion and Topology Consistency Guided Learning for Mitral Valve Segmentationin 4D Ultrasound

Horus: A Protocol for Trustless Delegation Under Uncertainty

Mixture of Reasonings: Teach Large Language Models to Reason with Adaptive Strategies

Benchmarking Generalizable Bimanual Manipulation: RoboTwin Dual-Arm Collaboration Challenge at CVPR 2025 MEIS Workshop

Red Teaming for Generative AI, Report on a Copyright-Focused Exercise Completed in an Academic Medical Center

AirV2X: Unified Air-Ground Vehicle-to-Everything Collaboration

Semantic Structure-Aware Generative Attacks for Enhanced Adversarial Transferability

Aligning Frozen LLMs by Reinforcement Learning: An Iterative Reweight-then-Optimize Approach

Distinguishing Predictive and Generative AI in Regulation

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

Text-Aware Image Restoration with Diffusion Models

How Good LLM-Generated Password Policies Are?

Towards an Explainable Comparison and Alignment of Feature Embeddings

Gradient-Based Model Fingerprinting for LLM Similarity Detection and Family Classification

Empowering Intelligent Low-altitude Economy with Large AI Model Deployment

Incorporating LLMs for Large-Scale Urban Complex Mobility Simulation

Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series

Traveling Across Languages: Benchmarking Cross-Lingual Consistency in Multimodal LLMs

Threat Modeling for AI: The Case for an Asset-Centric Approach

SoccerDiffusion: Toward Learning End-to-End Humanoid Robot Soccer from Gameplay Recordings

PAD: Phase-Amplitude Decoupling Fusion for Multi-Modal Land Cover Classification

Significativity Indices for Agreement Values

Transferrable Surrogates in Expressive Neural Architecture Search Spaces

Privacy-Preserving Operating Room Workflow Analysis using Digital Twins

Uncertainty-Guided Coarse-to-Fine Tumor Segmentation with Anatomy-Aware Post-Processing

CMD-HAR: Cross-Modal Disentanglement for Wearable Human Activity Recognition

Commander-GPT: Fully Unleashing the Sarcasm Detection Capability of Multi-Modal Large Language Models

Understanding-informed Bias Mitigation for Fair CMR Segmentation

HAPI: A Model for Learning Robot Facial Expressions from Human Preferences

MaizeField3D: A Curated 3D Point Cloud and Procedural Model Dataset of Field-Grown Maize from a Diversity Panel

Illuminant and light direction estimation using Wasserstein distance method

Fundamental Limits of Hierarchical Secure Aggregation with Cyclic User Association

LLM-Powered Prediction of Hyperglycemia and Discovery of Behavioral Treatment Pathways from Wearables and Diet

Interleaved Gibbs Diffusion: Generating Discrete-Continuous Data with Implicit Constraints

EquiTabPFN: A Target-Permutation Equivariant Prior Fitted Networks

Circuit-tuning: A Mechanistic Approach for Identifying Parameter Redundancy and Fine-tuning Neural Networks

EigenLoRAx: Recycling Adapters to Find Principal Subspaces for Resource-Efficient Adaptation and Inference

Learning Traffic Anomalies from Generative Models on Real-Time Observations

Enabling Population-Level Parallelism in Tree-Based Genetic Programming for Comprehensive GPU Acceleration

Parameters vs FLOPs: Scaling Laws for Optimal Sparsity for Mixture-of-Experts Language Models

Quantifying the Importance of Data Alignment in Downstream Model Performance

Quantum-enhanced causal discovery for a small number of samples

On Characterizations for Language Generation: Interplay of Hallucinations, Breadth, and Stability

Token Prepending: A Training-Free Approach for Eliciting Better Sentence Embeddings from LLMs

COEF-VQ: Cost-Efficient Video Quality Understanding through a Cascaded Multimodal LLM Framework

GeMID: Generalizable Models for IoT Device Identification

Next-Token Prediction Task Assumes Optimal Data Ordering for LLM Training in Proof Generation

Is Complex Query Answering Really Complex?

Aerial Vision-and-Language Navigation via Semantic-Topo-Metric Representation Guided LLM Reasoning

Offline Reinforcement Learning for Learning to Dispatch for Job Shop Scheduling

Reconsidering the energy efficiency of spiking neural networks

Exploring the Integration of Large Language Models in Industrial Test Maintenance Processes

Sequence-aware Pre-training for Echocardiography Probe Movement Guidance

Anatomical Foundation Models for Brain MRIs

Learning From Crowdsourced Noisy Labels: A Signal Processing Perspective

Quantifying the Cross-sectoral Intersecting Discrepancies within Multiple Groups Using Latent Class Analysis Towards Fairness

Delving into LLM-assisted writing in biomedical publications through excess vocabulary

Towards a Novel Measure of User Trust in XAI Systems

Avoiding Catastrophe in Online Learning by Asking for Help

Improving the Robustness of Distantly-Supervised Named Entity Recognition via Uncertainty-Aware Teacher Learning and Student-Student Collaborative Learning

Beyond Scale: The Diversity Coefficient as a Data Quality Metric for Variability in Natural Language Data

Kernel Density Bayesian Inverse Reinforcement Learning

Embodied AI Agents: Modeling the World

Mind2Web 2: Evaluating Agentic Search with Agent-as-a-Judge

AI Flow: Perspectives, Scenarios, and Approaches

A framework for Conditional Reasoning in Answer Set Programming

Autoformalization in the Era of Large Language Models: A Survey

Agentic AI Process Observability: Discovering Behavioral Variability

Artificial Intelligence Index Report 2025

MAPS: Advancing Multi-Modal Reasoning in Expert-Level Physical Science

XGeM: A Multi-Prompt Foundation Model for Multimodal Medical Data Generation

Direct Preference Optimization Using Sparse Feature-Level Constraints

Unsupervised Cognition

Urban Region Pre-training and Prompting: A Graph-based Approach

Road Graph Generator: Mapping roads at construction sites from GPS データ

Point3R: Streaming 3D Reconstruction with Explicit Spatial Pointer Memory

LiteReality: Graphics-Ready 3D Scene Reconstruction from RGB-D Scans

Answer Matching Outperforms Multiple Choice for Language Model Evaluation

Subtyping in DHOL - Extended preprint

MOTIF: Modular Thinking via Reinforcement Fine-tuning in LLMs

USAD: An Unsupervised Data Augmentation Spatio-Temporal Attention Diffusion Network

DNN-Based Precoding in RIS-Aided mmWave MIMO Systems With Practical Phase Shift

SynapseRoute: An Auto-Route Switching Framework on Dual-State Large Language Model

Self-Correction Bench: Revealing and Addressing the Self-Correction Blind Spot in LLMs

Multi-agent Auditory Scene Analysis

Fast and Simplex: 2-Simplicial Attention in Triton

Synthesizable by Design: A Retrosynthesis-Guided Framework for Molecular Analog Generation

Linear Attention with Global Context: A Multipole Attention Mechanism for Vision and Physics

Early Signs of Steganographic Capabilities in Frontier LLMs

Meta SecAlign: A Secure Foundation LLM Against Prompt Injection Attacks

FairHuman: Boosting Hand and Face Quality in Human Image Generation with Minimum Potential Delay Fairness in Diffusion Models

APT: Adaptive Personalized Training for Diffusion Models with Limited Data

ASDA: Audio Spectrogram Differential Attention Mechanism for Self-Supervised Representation Learning

Quantifying the Importance of Data Alignment in Downstream Model Performance

Created by

Haebom

作者

Krrish Chawla, Aryan Sahai, Mario DePavia, Sudharsan Sundar, Brando Miranda, Elyas Obbad, Sanmi Koyejo

概要

この論文は、大規模言語モデル（LLM）学習におけるデータサイズの重要性に関する従来の観点とは異なり、見落とされやすいデータ品質の面である照合の役割を探ります。 Task2Vecベースのソート係数を使用して、2つのデータセット間の類似性を定量的に測定し、学習データと評価データ間のソートがパフォーマンスに与える影響を分析します。 2つの設定（1.さまざまな事前学習（pt）データセットと評価データセット間のアライメント係数の増加の影響、2.ドメイン固有の微調整（ft）データセットとドメイン固有の評価データセット間のアライメント係数の増加の影響）の制御された実験を実行します。自動定式化（自然言語と形式検証のためのコード間の機械翻訳作業）と呼ばれるドメイン固有の作業により、モデルの学習および評価データのソート係数とモデルの損失/多重性の間の強力で予測可能な負の相関関係が見つかります。これは、特に自動定式化などの特殊なサブタスクでは、データ量よりもデータの並べ替えの重要性を示し、LLM学習アプローチの再評価を提案します。

Takeaways、Limitations

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Takeaways：

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LLM学習では、照合順序がデータ量と同じくらい重要であることを示しています。

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特に、ドメイン固有のサブタスクにおける照合順序の重要性を強調します。

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Task2Vecベースのソート係数を利用したデータソートの測定と分析方法の提示

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LLM学習アプローチの再評価の必要性の提示

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Limitations：

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自動定式化と呼ばれる特定のドメインに限定された研究結果。他のドメインでも同じ結果が現れるかどうかをさらに研究する必要があります。

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Task2Vecベースのソート係数の一般性と限界の追加レビューが必要です。

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照合順序を改善するための具体的な方法論の提示の欠如。

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