Daily Arxiv

전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
본 페이지는 Google Gemini를 활용해 요약 정리하며, 비영리로 운영 됩니다.
논문에 대한 저작권은 저자 및 해당 기관에 있으며, 공유 시 출처만 명기하면 됩니다.

SWE-Compass: Towards Unified Evaluation of Agentic Coding Abilities for Large Language Models

Perceptually Aligning Representations of Music via Noise-Augmented Autoencoders

Rethinking Metrics and Diffusion Architecture for 3D Point Cloud Generation

DeepEyesV2: Toward Agentic Multimodal Model

MERaLiON-SER: Robust Speech Emotion Recognition Model for English and SEA Languages

Minimal and Mechanistic Conditions for Behavioral Self-Awareness in LLMs

multiMentalRoBERTa: A Fine-tuned Multiclass Classifier for Mental Health Disorder

Addressing divergent representations from causal interventions on neural networks

Alternative Fairness and Accuracy Optimization in Criminal Justice

Leveraging LLM-based agents for social science research: insights from citation network simulations

Conversational Collective Intelligence (CCI) using Hyperchat AI in a Real-world Forecasting Task

Explaining Human Choice Probabilities with Simple Vector Representations

Development of the Bioinspired Tendon-Driven DexHand 021 with Proprioceptive Compliance Control

GraphCliff: Short-Long Range Gating for Subtle Differences but Critical Changes

AutoAdv: Automated Adversarial Prompting for Multi-Turn Jailbreaking of Large Language Models

MM-UNet: Morph Mamba U-shaped Convolutional Networks for Retinal Vessel Segmentation

RobustFSM: Submodular Maximization in Federated Setting with Malicious Clients

Tool Zero: Training Tool-Augmented LLMs via Pure RL from Scratch

Reg-DPO: SFT-Regularized Direct Preference Optimization with GT-Pair for Improving Video Generation

Reasoning Planning for Language Models

Aligning Brain Signals with Multimodal Speech and Vision Embeddings

Calibrating and Rotating: A Unified Framework for Weight Conditioning in PEFT

FedAdamW: A Communication-Efficient Optimizer with Convergence and Generalization Guarantees for Federated Large Models

Beyond Data Scarcity Optimizing R3GAN for Medical Image Generation from Small Datasets

Inside CORE-KG: Evaluating Structured Prompting and Coreference Resolution for Knowledge Graphs

Learning from N-Tuple Data with M Positive Instances: Unbiased Risk Estimation and Theoretical Guarantees

Universal Spectral Tokenization via Self-Supervised Panchromatic Representation Learning

DynaSpec: Context-aware Dynamic Speculative Sampling for Large-Vocabulary Language Models

Meronymic Ontology Extraction via Large Language Models

Robust Driving Control for Autonomous Vehicles: An Intelligent General-sum Constrained Adversarial Reinforcement Learning Approach

DPCformer: An Interpretable Deep Learning Model for Genomic Prediction in Crops

The Markovian Thinker

scUnified: An AI-Ready Standardized Resource for Single-Cell RNA Sequencing Analysis

Data-Centric Elastic Pipeline Parallelism for Efficient Long-Context LLM Training

Security-aware Semantic-driven ISAC via Paired Adversarial Residual Networks

TimeMosaic: Temporal Heterogeneity Guided Time Series Forecasting via Adaptive Granularity Patch and Segment-wise Decoding

Pure Vision Language Action (VLA) Models: A Comprehensive Survey

UniPixel: Unified Object Referring and Segmentation for Pixel-Level Visual Reasoning

Assisting the Grading of a Handwritten General Chemistry Exam with Artificial Intelligence

Privacy-Preserving Personalization in Education: A Federated Recommender System for Student Performance Prediction

UNO: Unifying One-stage Video Scene Graph Generation via Object-Centric Visual Representation Learning

Normality and the Turing Test

GUARD: Guideline Upholding Test through Adaptive Role-play and Jailbreak Diagnostics for LLMs

Cross-Platform E-Commerce Product Categorization and Recategorization: A Multimodal Hierarchical Classification Approach

ANO : Faster is Better in Noisy Landscape

Generative Medical Event Models Improve with Scale

ComoRAG: A Cognitive-Inspired Memory-Organized RAG for Stateful Long Narrative Reasoning

LLMCARE: early detection of cognitive impairment via transformer models enhanced by LLM-generated synthetic data

Oblivionis: A Lightweight Learning and Unlearning Framework for Federated Large Language Models

Rethinking Tokenization for Rich Morphology: The Dominance of Unigram over BPE and Morphological Alignment

Discovering Spatial Correlations of Earth Observations for weather forecasting by using Graph Structure Learning

Forecasting When to Forecast: Accelerating Diffusion Models with Confidence-Gated Taylor

Universal Neurons in GPT-2: Emergence, Persistence, and Functional Impact

Embedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine Learning

RaGS: Unleashing 3D Gaussian Splatting from 4D Radar and Monocular Cues for 3D Object Detection

Controllable Hybrid Captioner for Improved Long-form Video Understanding

Steering Out-of-Distribution Generalization with Concept Ablation Fine-Tuning

When Person Re-Identification Meets Event Camera: A Benchmark Dataset and An Attribute-guided Re-Identification Framework

Evaluating LLM-based Workflows for Switched-Mode Power Supply Design

Monitoring Risks in Test-Time Adaptation

White-Basilisk: A Hybrid Model for Code Vulnerability Detection

Bridging the Plausibility-Validity Gap by Fine-Tuning a Reasoning-Enhanced LLM for Chemical Synthesis and Discovery

CoRe: Benchmarking LLMs Code Reasoning Capabilities through Static Analysis Tasks

DP-Fusion: Token-Level Differentially Private Inference for Large Language Models

Meta SecAlign: A Secure Foundation LLM Against Prompt Injection Attacks

GPT, But Backwards: Exactly Inverting Language Model Outputs

Visual Structures Helps Visual Reasoning: Addressing the Binding Problem in VLMs

ReCode: Updating Code API Knowledge with Reinforcement Learning

Time-Prompt: Integrated Heterogeneous Prompts for Unlocking LLMs in Time Series Forecasting

Sekai: A Video Dataset towards World Exploration

Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion

Towards Competent AI for Fundamental Analysis in Finance: A Benchmark Dataset and Evaluation

A Fairness-Aware Strategy for B5G Physical-layer Security Leveraging Reconfigurable Intelligent Surfaces

Bridging Weakly-Supervised Learning and VLM Distillation: Noisy Partial Label Learning for Efficient Downstream Adaptation

Data Leakage and Deceptive Performance: A Critical Examination of Credit Card Fraud Detection Methodologies

GRAM: Spatial general-purpose audio representation models for real-world applications

Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications

VideoCAD: A Dataset and Model for Learning Long-Horizon 3D CAD UI Interactions from Video

When Language Shapes Thought: Cross-Lingual Transfer of Factual Knowledge in Question Answering

Rethinking Text-based Protein Understanding: Retrieval or LLM?

Language Model Distillation: A Temporal Difference Imitation Learning Perspective

TextDiffuser-RL: Efficient and Robust Text Layout Optimization for High-Fidelity Text-to-Image Synthesis

Guided Diffusion Sampling on Function Spaces with Applications to PDEs

The Energy Cost of Reasoning: Analyzing Energy Usage in LLMs with Test-time Compute

DNOI-4DRO: Deep 4D Radar Odometry with Differentiable Neural-Optimization Iterations

When Bias Helps Learning: Bridging Initial Prejudice and Trainability

HumaniBench: A Human-Centric Framework for Large Multimodal Models Evaluation

X-Sim: Cross-Embodiment Learning via Real-to-Sim-to-Real

Environment-Aware Indoor LoRaWAN Ranging Using Path Loss Model Inversion and Adaptive RSSI Filtering

Video CLIP Model for Multi-View Echocardiography Interpretation

Quantum Doubly Stochastic Transformers

ColorBench: Can VLMs See and Understand the Colorful World? A Comprehensive Benchmark for Color Perception, Reasoning, and Robustness

Parameter-Free Fine-tuning via Redundancy Elimination for Vision Foundation Models

Accelerating LLM Inference Throughput via Asynchronous KV Cache Prefetching

MultiMed-ST: Large-scale Many-to-many Multilingual Medical Speech Translation

How Post-Training Reshapes LLMs: A Mechanistic View on Knowledge, Truthfulness, Refusal, and Confidence

On the Consistency of Multilingual Context Utilization in Retrieval-Augmented Generation

Quantitative Evaluation of Quantum/Classical Neural Network Using a Game Solver Metric

Simulator Ensembles for Trustworthy Autonomous Driving Testing

Continual Pre-training of MoEs: How robust is your router?

Hatred Stems from Ignorance! Distillation of the Persuasion Modes in Countering Conversational Hate Speech

Created by

Haebom

저자

Ghadi Alyahya, Abeer Aldayel

개요

본 논문은 온라인 혐오 발언 대응의 최적 방법을 이해하기 위해 반박 발언(counterspeech)에 사용되는 요소들을 분석합니다. 기존 연구에서 감정적 공감, 공격성, 적대감 등 감정적 요소를 중심으로 연구되었던 반면, 본 연구는 설득 방식을 이성, 감정, 신뢰도 세 가지로 구분하여 분석합니다. 인종차별, 성차별, 종교 편견과 관련된 폐쇄형(다회차) 및 개방형(단회차) 대화 상황에서 인간과 기계가 생성한 반박 발언의 설득 방식 사용을 비교 분석하고, 반박 발언의 입장과 설득 방식 간 상호 작용을 평가합니다. 특히, 폐쇄형 및 개방형 대화 상황에서 사용되는 설득 방식에 미묘한 차이가 있으며, 일반적으로 이성적 설득 방식이 혐오 발언에 대한 반박으로 더 많이 사용되는 것을 확인했습니다. 기계 생성 반박 발언은 감정적 설득 방식을 더 많이 사용하는 반면, 인간은 이성적 설득 방식을 더 선호하는 경향을 보였으며, 이성적 설득 방식이 다른 방식보다 더 많은 지지를 얻는 것으로 나타났습니다. 본 연구는 혐오 발언 대응 연구에 설득 방식을 통합하는 것이 설명력을 높이고 최적의 반박 발언 구성 요소 평가에 도움이 될 수 있음을 시사합니다.

시사점, 한계점

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시사점:

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온라인 혐오 발언 대응에 있어 이성적 설득 방식의 효과성을 제시합니다.

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인간과 기계 생성 반박 발언의 차이점을 밝히고, 최적의 반박 발언 생성을 위한 시사점을 제공합니다.

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설득 방식을 분석 틀로 활용하여 혐오 발언 대응 연구의 설명력을 높일 수 있음을 보여줍니다.

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폐쇄형 및 개방형 대화 상황에서의 반박 발언 전략의 차이를 제시합니다.

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한계점:

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연구 대상 데이터의 범위와 일반화 가능성에 대한 추가적인 검토가 필요합니다.

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설득 방식 이외의 다른 요소(예: 언어적 표현 방식, 맥락)의 영향에 대한 고려가 부족할 수 있습니다.

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기계 생성 반박 발언의 성능 향상을 위한 구체적인 방안 제시가 미흡합니다.

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