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.

Entropy Controllable Direct Preference Optimization

TrajAgent: An LLM-based Agent Framework for Automated Trajectory Modeling via Collaboration of Large and Small Models

Control Industrial Automation System with Large Language Model Agents

How Well Do Large Language Models Serve as End-to-End Secure Code Agents for Python?

Self-interpreting Adversarial Images

Evaluating Implicit Bias in Large Language Models by Attacking From a Psychometric Perspective

Ad Auctions for LLMs via Retrieval Augmented Generation

Dynamic and Adaptive Feature Generation with LLM

MiniMaxAD: A Lightweight Autoencoder for Feature-Rich Anomaly Detection

PLeak: Prompt Leaking Attacks against Large Language Model Applications

HandS3C: 3D Hand Mesh Reconstruction with State Space Spatial Channel Attention from RGB images

DiffTORI: Differentiable Trajectory Optimization for Deep Reinforcement and Imitation Learning

Evolution Guided Generative Flow Networks

Manipulating Feature Visualizations with Gradient Slingshots

Scientists' First Exam: Probing Cognitive Abilities of MLLM via Perception, Understanding, and Reasoning

Agent Semantics, Semantic Spacetime, and Graphical Reasoning

Decomposability-Guaranteed Cooperative Coevolution for Large-Scale Itinerary Planning

DeePoly: A High-Order Accuracy Scientific Machine Learning Framework for Function Approximation and Solving PDEs

Does Thinking More always Help? Understanding Test-Time Scaling in Reasoning Models

The Automated but Risky Game: Modeling Agent-to-Agent Negotiations and Transactions in Consumer Markets

ChemRxivQuest: A Curated Chemistry Question-Answer Database Extracted from ChemRxiv Preprints

Epistemic Artificial Intelligence is Essential for Machine Learning Models to Truly `Know When They Do Not Know'

Enhancing multimodal analogical reasoning with Logic Augmented Generation

Beating Transformers using Synthetic Cognition

HypRL: Reinforcement Learning of Control Policies for Hyperproperties

From Idea to Implementation: Evaluating the Influence of Large Language Models in Software Development -- An Opinion Paper

Bel Esprit: Multi-Agent Framework for Building AI Model Pipelines

Dynamic Policy Fusion for User Alignment Without Re-Interaction

Tell Me What You Don't Know: Enhancing Refusal Capabilities of Role-Playing Agents via Representation Space Analysis and Editing

An overview of domain-specific foundation model: key technologies, applications and challenges

Learning from Litigation: Graphs and LLMs for Retrieval and Reasoning in eDiscovery

Combining Deep Reinforcement Learning and Search with Generative Models for Game-Theoretic Opponent Modeling

EMLoC: Emulator-based Memory-efficient Fine-tuning with LoRA Correction

code_transformed: The Influence of Large Language Models on Code

Reimagining Dance: Real-time Music Co-creation between Dancers and AI

Upgrade or Switch: Do We Need a New Registry Architecture for the Internet of AI Agents?

VGR: Visual Grounded Reasoning

Technical Evaluation of a Disruptive Approach in Homomorphic AI

SAIL: Faster-than-Demonstration Execution of Imitation Learning Policies

Subjective Experience in AI Systems: What Do AI Researchers and the Public Believe?

Today's Cat Is Tomorrow's Dog: Accounting for Time-Based Changes in the Labels of ML Vulnerability Detection Approaches

Improving Large Language Model Safety with Contrastive Representation Learning

LiveCodeBench Pro: How Do Olympiad Medalists Judge LLMs in Competitive Programming?

Real-World Deployment of a Lane Change Prediction Architecture Based on Knowledge Graph Embeddings and Bayesian Inference

Breaking Habits: On the Role of the Advantage Function in Learning Causal State Representations

Spectra-to-Structure and Structure-to-Spectra Inference Across the Periodic Table

A Neural Rejection System Against Universal Adversarial Perturbations in Radio Signal Classification

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices

Enter: Graduated Realism: A Pedagogical Framework for AI-Powered Avatars in Virtual Reality Teacher Training

An Explainable AI Framework for Dynamic Resource Management in Vehicular Network Slicing

Robust Molecular Property Prediction via Densifying Scarce Labeled Data

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?

MindGrab for BrainChop: Fast and Accurate Skull Stripping for Command Line and Browser

Regression-adjusted Monte Carlo Estimators for Shapley Values and Probabilistic Values

TrustGLM: Evaluating the Robustness of GraphLLMs Against Prompt, Text, and Structure Attacks

Diffusion-Based Electrocardiography Noise Quantification via Anomaly Detection

Abstract Sound Fusion with Unconditioned Inversion Model

Persona-driven Simulation of Voting Behavior in the European Parliament with Large Language Models

Why Do Class-Dependent Evaluation Effects Occur with Time Series Feature Attributions? A Synthetic Data Investigation

Self-supervised Learning of Echocardiographic Video Representations via Online Cluster Distillation

Real-Time Feedback and Benchmark Dataset for Isometric Pose Evaluation

FeNN: A RISC-V vector processor for Spiking Neural Network acceleration

Interaction, Process, Infrastructure: A Unified Architecture for Human-Agent Collaboration

Configurable Preference Tuning with Rubric-Guided Synthetic Data

Differential Privacy in Machine Learning: From Symbolic AI to LLMs

MTabVQA: Evaluating Multi-Tabular Reasoning of Language Models in Visual Space

LLMs on support of privacy and security of mobile apps: state of the art and research directions

Pose Matters: Evaluating Vision Transformers and CNNs for Human Action Recognition on Small COCO Subsets

Improving Causal Interventions in Amnesic Probing with Mean Projection or LEACE

Converting Annotated Clinical Cases into Structured Case Report Forms

DISCO: Mitigating Bias in Deep Learning with Conditional Distance Correlation

Robot Context Protocol (RCP): A Runtime-Agnostic Interface for Agent-Aware Robot Control

LoRA-Gen: Specializing Large Language Model via Online LoRA Generation

FAA Framework: A Large Language Model-Based Approach for Credit Card Fraud Investigations

Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression

Convergent Linear Representations of Emergent Misalignment

Model Organisms for Emergent Misalignment

Are LLMs Good Text Diacritizers? An Arabic and Yor\`ub\'a Case Study

GraphRAG-Causal: A novel graph-augmented framework for causal reasoning and annotation in news

A$^2$LC: Active and Automated Label Correction for Semantic Segmentation

OV-MAP : Open-Vocabulary Zero-Shot 3D Instance Segmentation Map for Robots

A Comparative Analysis of Influence Signals for Data Debugging

Learn to Preserve Personality: Federated Foundation Models in Recommendations

Identifying Helpful Context for LLM-based Vulnerability Repair: A Preliminary Study

Leveraging GPT-4 for Vulnerability-Witnessing Unit Test Generation

DaMO: A Data-Efficient Multimodal Orchestrator for Temporal Reasoning with Video LLMs

Improving Multimodal Learning Balance and Sufficiency through Data Remixing

FIMA-Q: Post-Training Quantization for Vision Transformers by Fisher Information Matrix Approximation

Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis

Investigating Vulnerabilities and Defenses Against Audio-Visual Attacks: A Comprehensive Survey Emphasizing Multimodal Models

Prioritizing Alignment Paradigms over Task-Specific Model Customization in Time-Series LLMs

Machine Learning-Based Quantification of Vesicoureteral Reflux with Enhancing Accuracy and Efficiency

Diabetes Prediction and Management Using Machine Learning Approaches

Composite Data Augmentations for Synthetic Image Detection Against Real-World Perturbations

Relational Schemata in BERT Are Inducible, Not Emergent: A Study of Performance vs. Competence in Language Models

LearnAlign: Reasoning Data Selection for Reinforcement Learning in Large Language Models Based on Improved Gradient Alignment

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer

Voxel-Level Brain States Prediction Using Swin Transformer

DPUV4E: High-Throughput DPU Architecture Design for CNN on Versal ACAP

KoGEC : Korean Grammatical Error Correction with Pre-trained Translation Models

Natural Language Processing in Support of Evidence-based Medicine: A Scoping Review

Created by

Haebom

저자

Zihan Xu, Haotian Ma, Gongbo Zhang, Yihao Ding, Chunhua Weng, Yifan Peng

개요

본 논문은 자연어 처리(NLP)를 이용한 근거중심의학(EBM) 연구 129편에 대한 심층 분석을 제시합니다. 의학 문헌의 급증과 큐레이션 비용 증가로 인해 NLP를 활용한 EBM 연구의 중요성이 커지고 있으며, 본 논문은 NLP가 EBM의 5단계(Ask, Acquire, Appraise, Apply, Assess)를 어떻게 지원하는지 체계적으로 탐구합니다. NLP를 통해 증거 추출, 증거 종합, 평가, 요약 및 데이터 이해도 향상, 효율적인 임상 워크플로우 구축 등을 개선하여 EBM을 혁신할 가능성을 제시하며, 동시에 현장의 한계점과 미래 연구 방향을 제안합니다.

시사점, 한계점

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시사점:

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NLP가 EBM의 5단계 전반에 걸쳐 효율성 및 정확성 향상에 기여할 수 있음을 보여줌.

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증거 추출, 종합, 평가, 요약 과정의 자동화를 통해 의료 전문가의 업무 부담 경감 및 의사결정 속도 향상 가능성 제시.

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데이터의 이해도를 높여 더욱 정확하고 효과적인 임상 워크플로우 구축 가능성 제시.

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EBM의 혁신을 위한 NLP의 잠재력을 확인하고 미래 연구 방향을 제시.

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한계점:

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논문에서 구체적으로 언급된 한계점은 제시되지 않았으나, NLP 기술의 발전 및 의학 데이터의 복잡성과 관련된 한계가 존재할 것으로 예상됨.

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모든 의학 분야에 걸쳐 일반화될 수 있는지에 대한 추가 연구 필요.

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윤리적, 법적 문제에 대한 고려 필요.

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