Daily Arxiv

전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
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GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning

Horus: A Protocol for Trustless Delegation Under Uncertainty

Reducing Variability of Multiple Instance Learning Methods for Digital Pathology

Positioning AI Tools to Support Online Harm Reduction Practice: Applications and Design Directions

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues

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

BioPars: A Pretrained Biomedical Large Language Model for Persian Biomedical Text Mining

TRACED: Transition-aware Regret Approximation with Co-learnability for Environment Design

Towards Safety Evaluations of Theory of Mind in Large Language Models

Fair Algorithms with Probing for Multi-Agent Multi-Armed Bandits

GraphGSOcc: Semantic-Geometric Graph Transformer with Dynamic-Static Decoupling for 3D Gaussian Splatting-based Occupancy Prediction

Screen Hijack: Visual Poisoning of VLM Agents in Mobile Environments

15,500 Seconds: Lean UAV Classification Leveraging PEFT and Pre-Trained Networks

Tightly-Coupled LiDAR-IMU-Leg Odometry with Online Learned Leg Kinematics Incorporating Foot Tactile Information

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers

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

On the Fundamental Impossibility of Hallucination Control in Large Language Models

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems

Real-Time Blind Defocus Deblurring for Earth Observation: The IMAGIN-e Mission Approach

Scaling Up Liquid-Resistance Liquid-Capacitance Networks for Efficient Sequence Modeling

Self-reflective Uncertainties: Do LLMs Know Their Internal Answer Distribution?

FastMamba: A High-Speed and Efficient Mamba Accelerator on FPGA with Accurate Quantization

Pre-training Large Memory Language Models with Internal and External Knowledge

Towards Universal Semantics With Large Language Models

Aitomia: Your Intelligent Assistant for AI-Driven Atomistic and Quantum Chemical Simulations

Enhancing Robustness to Missing Modalities through Clustered Federated Learning

Perceiving Beyond Language Priors: Enhancing Visual Comprehension and Attention in Multimodal Models

LZ Penalty: An information-theoretic repetition penalty for autoregressive language models

Towards Cardiac MRI Foundation Models: Comprehensive Visual-Tabular Representations for Whole-Heart Assessment and Beyond

TARO: Timestep-Adaptive Representation Alignment with Onset-Aware Conditioning for Synchronized Video-to-Audio Synthesis

Recursive Training Loops in LLMs: How training data properties modulate distribution shift in generated data?

Real-is-Sim: Bridging the Sim-to-Real Gap with a Dynamic Digital Twin

Concat-ID: Towards Universal Identity-Preserving Video Synthesis

How Metacognitive Architectures Remember Their Own Thoughts: A Systematic Review

SFO: Piloting VLM Feedback for Offline RL

Towards Efficient Educational Chatbots: Benchmarking RAG Frameworks

KatFishNet: Detecting LLM-Generated Korean Text through Linguistic Feature Analysis

Distribution Matching for Self-Supervised Transfer Learning

A Baseline Method for Removing Invisible Image Watermarks using Deep Image Prior

SKIL: Semantic Keypoint Imitation Learning for Generalizable Data-efficient Manipulation

AirRadar: Inferring Nationwide Air Quality in China with Deep Neural Networks

A Framework for Mining Collectively-Behaving Bots in MMORPGs

Continual Learning with Strategic Selection and Forgetting for Network Intrusion Detection

A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions

A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation

GenBFA: An Evolutionary Optimization Approach to Bit-Flip Attacks on LLMs

There and Back Again: On the relation between Noise and Image Inversions in Diffusion Models

Unified Triplet-Level Hallucination Evaluation for Large Vision-Language Models

Contrastive Learning and Adversarial Disentanglement for Privacy-Aware Task-Oriented Semantic Communication

Unsupervised Panoptic Interpretation of Latent Spaces in GANs Using Space-Filling Vector Quantization

NegMerge: Sign-Consensual Weight Merging for Machine Unlearning

Drug Discovery SMILES-to-Pharmacokinetics Diffusion Models with Deep Molecular Understanding

Sublinear Regret for a Class of Continuous-Time Linear-Quadratic Reinforcement Learning Problems

Backdooring Bias (B^2) into Stable Diffusion Models

Embodied Instruction Following in Unknown Environments

Improving Consistency Models with Generator-Augmented Flows

OralBBNet: Spatially Guided Dental Segmentation of Panoramic X-Rays with Bounding Box Priors

Divergent Creativity in Humans and Large Language Models

SpikeNAS: A Fast Memory-Aware Neural Architecture Search Framework for Spiking Neural Network-based Embedded AI Systems

Squat: Quant Small Language Models on the Edge

Dataset Distillation via the Wasserstein Metric

The Boolean Solution Problem from the Perspective of Predicate Logic -- Extended Version

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess

World-aware Planning Narratives Enhance Large Vision-Language Model Planner

Reasoning about Uncertainty: Do Reasoning Models Know When They Don't Know?

MMLU-Reason: Benchmarking Multi-Task Multi-modal Language Understanding and Reasoning

Adapting Probabilistic Risk Assessment for AI

Beating Transformers using Synthetic Cognition

MedAgent-Pro: Towards Evidence-based Multi-modal Medical Diagnosis via Reasoning Agentic Workflow

Using Large Language Models to Categorize Strategic Situations and Decipher Motivations Behind Human Behaviors

ChartCoder: Advancing Multimodal Large Language Model for Chart-to-Code Generation

MCCoder: Streamlining Motion Control with LLM-Assisted Code Generation and Rigorous Verification

DREAMS: A python framework for Training Deep Learning Models on EEG Data with Model Card Reporting for Medical Applications

Human Mobility Modeling with Household Coordination Activities under Limited Information via Retrieval-Augmented LLMs

Combating Confirmation Bias: A Unified Pseudo-Labeling Framework for Entity Alignment

AC-DiT: Adaptive Coordination Diffusion Transformer for Mobile Manipulation

Locality-aware Parallel Decoding for Efficient Autoregressive Image Generation

How Well Does GPT-4o Understand Vision? Evaluating Multimodal Foundation Models on Standard Computer Vision Tasks

SpecCLIP: Aligning and Translating Spectroscopic Measurements for Stars

Adaptability of ASR Models on Low-Resource Language: A Comparative Study of Whisper and Wav2Vec-BERT on Bangla

Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection

End-to-End Large Portfolio Optimization for Variance Minimization with Neural Networks through Covariance Cleaning

Gradient-Adaptive Policy Optimization: Towards Multi-Objective Alignment of Large Language Models

AI4Research: A Survey of Artificial Intelligence for Scientific Research

Towards Foundation Auto-Encoders for Time-Series Anomaly Detection

Bridging UI Design and chatbot Interactions: Applying Form-Based Principles to Conversational Agents

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling

MILP-SAT-GNN: Yet Another Neural SAT Solver

Empowering Manufacturers with Privacy-Preserving AI Tools: A Case Study in Privacy-Preserving Machine Learning to Solve Real-World Problems

LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs

How Do Vision-Language Models Process Conflicting Information Across Modalities?

Are Vision Transformer Representations Semantically Meaningful? A Case Study in Medical Imaging

Probing Evaluation Awareness of Language Models

MuRating: A High Quality Data Selecting Approach to Multilingual Large Language Model Pretraining

BranchNet: A Neuro-Symbolic Learning Framework for Structured Multi-Class Classification

GPU-based complete search for nonlinear minimization subject to bounds

Enhanced Generative Model Evaluation with Clipped Density and Coverage

Tuning without Peeking: Provable Privacy and Generalization Bounds for LLM Post-Training

ECCV 2024 W-CODA: 1st Workshop on Multimodal Perception and Comprehension of Corner Cases in Autonomous Driving

Towards culturally-appropriate conversational AI for health in the majority world: An exploratory study with citizens and professionals in Latin America

Divergent Creativity in Humans and Large Language Models

Created by

Haebom

저자

Antoine Bellemare-Pepin (CoCo Lab, Psychology department, Universite de Montreal, Montreal, QC, Canada, Music department, Concordia University, Montreal, QC, Canada), Fran\c{c}ois Lespinasse (Sociology and Anthropology department, Concordia University, Montreal, QC, Canada), Philipp Tholke (CoCo Lab, Psychology department, Universite de Montreal, Montreal, QC, Canada), Yann Harel (CoCo Lab, Psychology department, Universite de Montreal, Montreal, QC, Canada), Kory Mathewson (Mila), Jay A. Olson (Department of Psychology, University of Toronto Mississauga, Mississauga, ON, Canada), Yoshua Bengio (Mila, Department of Computer Science and Operations Research, Universite de Montreal, Montreal, QC, Canada), Karim Jerbi (CoCo Lab, Psychology department, Universite de Montreal, Montreal, QC, Canada, UNIQUE Center)

개요

본 논문은 최근 급격히 발전한 대규모 언어 모델(LLM)의 창의성에 대한 논의에서 중요하게 간과되었던 요소인 의미적 다양성을 체계적으로 평가하고, 특히 인간의 발산적 사고와 비교 분석한 연구입니다. 최첨단 LLM과 10만 명의 인간 데이터셋을 활용하여 발산적 연상 과제에서 LLM의 성능을 평가한 결과, LLM이 평균적인 인간의 수준을 능가하고 창의적인 글쓰기 능력에 근접함을 발견했습니다. 하지만, 고도로 창의적인 인간의 수준에는 미치지 못하며, 현재 LLM이 극복하지 못하는 한계가 존재함을 확인했습니다. 인간과 기계의 벤치마킹 프레임워크를 통해 AI에 의한 인간 창작 노동 대체 논쟁에 대한 객관적인 척도를 제공하고, 프롬프트 디자인 및 하이퍼파라미터 조정과 같은 LLM의 의미적 다양성 향상 기법을 제시했습니다.

시사점, 한계점

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시사점:

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LLM이 평균적인 인간 수준을 넘어서는 의미적 다양성을 보일 수 있음을 실증적으로 보여줌.

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고도로 창의적인 인간과 LLM의 성능 차이를 객관적으로 측정하고 비교 분석하는 프레임워크 제시.

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LLM의 의미적 다양성 향상을 위한 구체적인 기술(프롬프트 디자인, 하이퍼파라미터 조정 등) 제시.

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AI에 의한 인간 창작 노동 대체 논쟁에 대한 객관적인 데이터 기반 분석 제공.

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인간과 AI의 창의적 사고의 차이점에 대한 심층적인 탐구 필요성 제기.

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한계점:

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현재 LLM은 고도로 창의적인 인간의 수준에는 미치지 못함.

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10만 명의 인간 데이터셋의 대표성 및 균질성에 대한 검토 필요.

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제시된 의미적 다양성 향상 기법의 일반화 가능성 및 효과에 대한 추가 연구 필요.

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