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.

BrowseConf: Confidence-Guided Test-Time Scaling for Web Agents

FRBNet: Revisiting Low-Light Vision through Frequency-Domain Radial Basis Network

Eigen-Value: Efficient Domain-Robust Data Valuation via Eigenvalue-Based Approach

Robust Uncertainty Quantification for Self-Evolving Large Language Models via Continual Domain Pretraining

HyPerNav: Hybrid Perception for Object-Oriented Navigation in Unknown Environment

TraceTrans: Translation and Spatial Tracing for Surgical Prediction

GRAID: Enhancing Spatial Reasoning of VLMs Through High-Fidelity Data Generation

CustomIR: Unsupervised Fine-Tuning of Dense Embeddings for Known Document Corpora

Your Dense Retriever is Secretly an Expeditious Reasoner

FieldGen: From Teleoperated Pre-Manipulation Trajectories to Field-Guided Data Generation

Context-level Language Modeling by Learning Predictive Context Embeddings

Detecting Latin in Historical Books with Large Language Models: A Multimodal Benchmark

MENTOR: A Reinforcement Learning Framework for Enabling Tool Use in Small Models via Teacher-Optimized Rewards

MemoryBench: A Benchmark for Memory and Continual Learning in LLM Systems

SimpleVSF: VLM-Scoring Fusion for Trajectory Prediction of End-to-End Autonomous Driving

The Formalism-Implementation Gap in Reinforcement Learning Research

OmniVinci: Enhancing Architecture and Data for Omni-Modal Understanding LLM

TokenTiming: A Dynamic Alignment Method for Universal Speculative Decoding Model Pairs

Cross-Scenario Unified Modeling of User Interests at Billion Scale

DPRF: A Generalizable Dynamic Persona Refinement Framework for Optimizing Behavior Alignment Between Personalized LLM Role-Playing Agents and Humans

Think Just Enough: Sequence-Level Entropy as a Confidence Signal for LLM Reasoning

SEER: The Span-based Emotion Evidence Retrieval Benchmark

Distilled Protein Backbone Generation

Untargeted Jailbreak Attack

AdaDetectGPT: Adaptive Detection of LLM-Generated Text with Statistical Guarantees

Prosperity before Collapse: How Far Can Off-Policy RL Reach with Stale Data on LLMs?

On Robustness of Vision-Language-Action Model against Multi-Modal Perturbations

PEARL: Peer-Enhanced Adaptive Radio via On-Device LLM

Seeing Symbols, Missing Cultures: Probing Vision-Language Models' Reasoning on Fire Imagery and Cultural Meaning

ImageNet-trained CNNs are not biased towards texture: Revisiting feature reliance through controlled suppression

PTQTP: Post-Training Quantization to Trit-Planes for Large Language Models

The human-machine paradox: how collaboration creates or destroys value, and why augmentation is key to resolving it

Reproducible workflow for online AI in digital health

Pre-trained knowledge elevates large language models beyond traditional chemical reaction optimizers

MolErr2Fix: Benchmarking LLM Trustworthiness in Chemistry via Modular Error Detection, Localization, Explanation, and Revision

Robustness is Important: Limitations of LLMs for Data Fitting

DP-LLM: Runtime Model Adaptation with Dynamic Layer-wise Precision Assignment

FoGE: Fock Space inspired encoding for graph prompting

PULSE: Practical Evaluation Scenarios for Large Multimodal Model Unlearning

GEMeX-RMCoT: An Enhanced Med-VQA Dataset for Region-Aware Multimodal Chain-of-Thought Reasoning

Thermometry of simulated Bose--Einstein condensates using machine learning

LittleBit: Ultra Low-Bit Quantization via Latent Factorization

BNMusic: Blending Environmental Noises into Personalized Music

Evaluating AI-Powered Learning Assistants in Engineering Higher Education: Student Engagement, Ethical Challenges, and Policy Implications

Mixture-of-Experts Meets In-Context Reinforcement Learning

Improving Data Efficiency for LLM Reinforcement Fine-tuning Through Difficulty-targeted Online Data Selection and Rollout Replay

NOBLE -- Neural Operator with Biologically-informed Latent Embeddings to Capture Experimental Variability in Biological Neuron Models

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

REASONING COMPILER: LLM-Guided Optimizations for Efficient Model Serving

PVP: An Image Dataset for Personalized Visual Persuasion with Persuasion Strategies, Viewer Characteristics, and Persuasiveness Ratings

FALCON: An ML Framework for Fully Automated Layout-Constrained Analog Circuit Design

OmniResponse: Online Multimodal Conversational Response Generation in Dyadic Interactions

GraSS: Scalable Data Attribution with Gradient Sparsification and Sparse Projection

MixAT: Combining Continuous and Discrete Adversarial Training for LLMs

STree: Speculative Tree Decoding for Hybrid State-Space Models

Do Language Models Use Their Depth Efficiently?

A Generalized Label Shift Perspective for Cross-Domain Gaze Estimation

The Logical Expressiveness of Temporal GNNs via Two-Dimensional Product Logics

Group-in-Group Policy Optimization for LLM Agent Training

BRIDGE: Benchmarking Large Language Models for Understanding Real-world Clinical Practice Text

Offline Learning and Forgetting for Reasoning with Large Language Models

Multimodal 3D Genome Pre-training

Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model

Mirror Descent and Novel Exponentiated Gradient Algorithms Using Trace-Form Entropies and Deformed Logarithms

Benchmarking AI Models in Software Engineering: A Review, Search Tool, and Unified Approach for Elevating Benchmark Quality

Generalized Exponentiated Gradient Algorithms Using the Euler Two-Parameter Logarithm

FragFM: Hierarchical Framework for Efficient Molecule Generation via Fragment-Level Discrete Flow Matching

ADMN: A Layer-Wise Adaptive Multimodal Network for Dynamic Input Noise and Compute Resources

A High-Dimensional Statistical Method for Optimizing Transfer Quantities in Multi-Source Transfer Learning

$\beta$-DQN: Improving Deep Q-Learning By Evolving the Behavior

Provable Scaling Laws for the Test-Time Compute of Large Language Models

Learned, Lagged, LLM-splained: LLM Responses to End User Security Questions

One-Step is Enough: Sparse Autoencoders for Text-to-Image Diffusion Models

TrajAgent: An LLM-Agent Framework for Trajectory Modeling via Large-and-Small Model Collaboration

GRS: Generating Robotic Simulation Tasks from Real-World Images

Navigation with VLM framework: Towards Going to Any Language

Retrieval-Augmented Generation-based Relation Extraction

Diffusion Models Meet Contextual Bandits

Querying Inconsistent Prioritized Data with ORBITS: Algorithms, Implementation, and Experiments

Multi-Agent Evolve: LLM Self-Improve through Co-evolution

ReCode: Unify Plan and Action for Universal Granularity Control

Human-Like Goalkeeping in a Realistic Football Simulation: a Sample-Efficient Reinforcement Learning Approach

From Prompt Optimization to Multi-Dimensional Credibility Evaluation: Enhancing Trustworthiness of Chinese LLM-Generated Liver MRI Reports

Huxley-G\"odel Machine: Human-Level Coding Agent Development by an Approximation of the Optimal Self-Improving Machine

Understanding AI Trustworthiness: A Scoping Review of AIES & FAccT Articles

PanicToCalm: A Proactive Counseling Agent for Panic Attacks

A Comprehensive Survey on Reinforcement Learning-based Agentic Search: Foundations, Roles, Optimizations, Evaluations, and Applications

Co-TAP: Three-Layer Agent Interaction Protocol Technical Report

Evaluating the Use of Large Language Models as Synthetic Social Agents in Social Science Research

MathBode: Understanding LLM Reasoning with Dynamical Systems

Is It Certainly a Deepfake? Reliability Analysis in Detection & Generation Ecosystem

Accelerate Scaling of LLM Finetuning via Quantifying the Coverage and Depth of Instruction Set

Freeze and Conquer: Reusable Ansatz for Solving the Traveling Salesman Problem

A Neuroscience-Inspired Dual-Process Model of Compositional Generalization

Memory Mosaics at scale

The Confidence Paradox: Can LLM Know When It's Wrong

VIRAL: Vision-grounded Integration for Reward design And Learning

Partner Modelling Emerges in Recurrent Agents (But Only When It Matters)

Multimodal Dreaming: A Global Workspace Approach to World Model-Based Reinforcement Learning

TableTime: Reformulating Time Series Classification as Training-Free Table Understanding with Large Language Models

Generative Large Language Models (gLLMs) in Content Analysis: A Practical Guide for Communication Research

Created by

Haebom

저자

Daria Kravets-Meinke, Hannah Schmid-Petri, Sonja Niemann, Ute Schmid

개요

ChatGPT와 같은 생성형 대규모 언어 모델(gLLMs)은 내용 분석을 위해 커뮤니케이션 연구에 점점 더 많이 사용되고 있습니다. 연구에 따르면 gLLMs는 커뮤니케이션 과학과 관련된 다양한 코딩 작업에서 크라우드 워커와 연구 조교와 같은 숙련된 코더보다 성능이 뛰어나며, 시간과 비용도 훨씬 적게 듭니다. 또한 gLLMs는 암시적인 의미와 맥락 정보를 해독하고, 자연어를 사용하여 지시할 수 있으며, 기본적인 프로그래밍 기술만으로도 배포할 수 있으며, 검증 데이터 세트를 제외하고는 거의 또는 전혀 주석이 달린 데이터가 필요하지 않으므로 자동화된 내용 분석에 있어서 패러다임 전환을 나타냅니다. 잠재력에도 불구하고, gLLMs를 커뮤니케이션 연구의 방법론적 도구 키트에 통합하는 것은 아직 개발되지 않았습니다. gLLM 지원 정량적 내용 분석에서 연구자는 결과 품질에 영향을 미치는 최소 7가지의 중요한 과제, 즉 (1) 코드북 개발, (2) 프롬프트 엔지니어링, (3) 모델 선택, (4) 매개변수 조정, (5) 반복적 개선, (6) 모델의 신뢰성 검증, (7) 성능 향상(선택 사항)을 해결해야 합니다. 본 논문은 gLLM 지원 정량적 내용 분석에 대한 새로운 연구를 종합하고 이러한 과제를 해결하기 위한 포괄적인 모범 사례 가이드를 제안합니다. 우리의 목표는 gLLM 기반 내용 분석을 더 많은 커뮤니케이션 연구자들에게 접근 가능하게 하고, 타당성, 신뢰성, 재현성 및 연구 윤리에 대한 확립된 학문적 품질 표준을 준수하도록 보장하는 것입니다.

시사점, 한계점

•

시사점:

◦

gLLMs는 기존의 내용 분석 방법론보다 시간과 비용 효율적인 대안을 제시합니다.

◦

gLLMs는 암시적 의미와 맥락 정보를 파악하는 데 특화되어 있어 분석의 깊이를 더할 수 있습니다.

◦

gLLMs는 자연어 기반 지시를 통해 접근성을 높여, 프로그래밍 기술이 부족한 연구자도 활용 가능하게 합니다.

◦

gLLMs 기반의 내용 분석은 검증 데이터 세트를 제외하고는 추가적인 주석 데이터가 거의 필요하지 않습니다.

◦

본 연구는 gLLM 기반 내용 분석의 모범 사례 가이드를 제시하여, 연구의 품질과 재현성을 보장합니다.

•

한계점:

◦

gLLMs의 결과 품질에 영향을 미치는 7가지 주요 과제(코드북 개발, 프롬프트 엔지니어링 등)를 해결해야 합니다.

◦

gLLMs의 활용은 아직 초기 단계이며, 커뮤니케이션 연구 분야에서 널리 사용되기 위해서는 추가적인 연구가 필요합니다.

◦

gLLMs의 신뢰성, 타당성, 재현성, 연구 윤리에 대한 엄격한 기준을 준수해야 합니다.

Made with Slashpage