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.

Exploring Diffusion Transformer Designs via Grafting

Teaming in the AI Era: AI-Augmented Frameworks for Forming, Simulating, and Optimizing Human Teams

ECoRAG: Evidentiality-guided Compression for Long Context RAG

Dissecting Bias in LLMs: A Mechanistic Interpretability Perspective

Does It Make Sense to Speak of Introspection in Large Language Models?

Sparse Autoencoders, Again?

Feature-Based Lie Group Transformer for Real-World Applications

TracLLM: A Generic Framework for Attributing Long Context LLMs

SemiOccam: A Robust Semi-Supervised Image Recognition Network Using Sparse Labels

Labelling Data with Unknown References

Tug-of-war between idiom's figurative and literal meanings in LLMs

State-Covering Trajectory Stitching for Diffusion Planners

Deep Learning Weather Models for Subregional Ocean Forecasting: A Case Study on the Canary Current Upwelling System

DORAEMON: Decentralized Ontology-aware Reliable Agent with Enhanced Memory Oriented Navigation

Subspecialty-Specific Foundation Model for Intelligent Gastrointestinal Pathology

RepoMaster: Autonomous Exploration and Understanding of GitHub Repositories for Complex Task Solving

An Uncertainty-Aware ED-LSTM for Probabilistic Suffix Prediction

SageAttention2++: A More Efficient Implementation of SageAttention2

Autocomp: LLM-Driven Code Optimization for Tensor Accelerators

IDA-Bench: Evaluating LLMs on Interactive Guided Data Analysis

Decoupling Representation and Learning in Genetic Programming: the LaSER Approach

Common Data Format (CDF): A Standardized Format for Match-Data in Football (Soccer)

Web Intellectual Property at Risk: Preventing Unauthorized Real-Time Retrieval by Large Language Models

How can Diffusion Models Evolve into Continual Generators?

Open Your Eyes: Vision Enhances Message Passing Neural Networks in Link Prediction

m-KAILIN: Knowledge-Driven Agentic Scientific Corpus Distillation Framework for Biomedical Large Language Models Training

FinSage: A Multi-aspect RAG System for Financial Filings Question Answering

Not All Rollouts are Useful: Down-Sampling Rollouts in LLM Reinforcement Learning

LauraTSE: Target Speaker Extraction using Auto-Regressive Decoder-Only Language Models

Reasoning Towards Fairness: Mitigating Bias in Language Models through Reasoning-Guided Fine-Tuning

Sparse Autoencoders Learn Monosemantic Features in Vision-Language Models

Multivariate Temporal Regression at Scale: A Three-Pillar Framework Combining ML, XAI, and NLP

GENIUS: A Generative Framework for Universal Multimodal Search

TinySQL: A Progressive Text-to-SQL Dataset for Mechanistic Interpretability Research

ARMOR: Empowering Multimodal Understanding Model with Interleaved Multimodal Generation Capability

A Survey on Sparse Autoencoders: Interpreting the Internal Mechanisms of Large Language Models

Knowledge Retention for Continual Model-Based Reinforcement Learning

Adversarial Tokenization

UDora: A Unified Red Teaming Framework against LLM Agents by Dynamically Hijacking Their Own Reasoning

SAGE: A Framework of Precise Retrieval for RAG

Graph Attention Networks Unleashed: A Fast and Explainable Vulnerability Assessment Framework for Microgrids

SafeAuto: Knowledge-Enhanced Safe Autonomous Driving with Multimodal Foundation Models

Emergent Symbolic Mechanisms Support Abstract Reasoning in Large Language Models

Improving Customer Service with Automatic Topic Detection in User Emails

DualSpec: Text-to-spatial-audio Generation via Dual-Spectrogram Guided Diffusion Model

Jacobian Sparse Autoencoders: Sparsify Computations, Not Just Activations

SpargeAttention: Accurate and Training-free Sparse Attention Accelerating Any Model Inference

MimeQA: Towards Socially-Intelligent Nonverbal Foundation Models

A Comprehensive Survey on Concept Erasure in Text-to-Image Diffusion Models

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws

Maximum Entropy Reinforcement Learning with Diffusion Policy

TituLLMs: A Family of Bangla LLMs with Comprehensive Benchmarking

Relational Conformal Prediction for Correlated Time Series

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models

UniDB: A Unified Diffusion Bridge Framework via Stochastic Optimal Control

The Complexity of Learning Sparse Superposed Features with Feedback

Peri-LN: Revisiting Normalization Layer in the Transformer Architecture

Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods

An Optimal Cascade Feature-Level Spatiotemporal Fusion Strategy for Anomaly Detection in CAN Bus

ProofAug: Efficient Neural Theorem Proving via Fine-grained Proof Structure Analysis

FDLLM: A Dedicated Detector for Black-Box LLMs Fingerprinting

The Bakers and Millers Game with Restricted Locations

Diving into Self-Evolving Training for Multimodal Reasoning

Reasoning Through Execution: Unifying Process and Outcome Rewards for Code Generation

A Riemannian Optimization Perspective of the Gauss-Newton Method for Feedforward Neural Networks

CoopetitiveV: Leveraging LLM-powered Coopetitive Multi-Agent Prompting for High-quality Verilog Generation

TraceVLA: Visual Trace Prompting Enhances Spatial-Temporal Awareness for Generalist Robotic Policies

The Synergy of LLMs & RL Unlocks Offline Learning of Generalizable Language-Conditioned Policies with Low-fidelity Data

Understanding Memorization in Generative Models via Sharpness in Probability Landscapes

A Cognac shot to forget bad memories: Corrective Unlearning in GNNs

Fundamental Limits of Prompt Tuning Transformers: Universality, Capacity and Efficiency

Who Can Withstand Chat-Audio Attacks? An Evaluation Benchmark for Large Audio-Language Models

CLIPErase: Efficient Unlearning of Visual-Textual Associations in CLIP

The Impact of Inference Acceleration on Bias of LLMs

pLDDT-Predictor: High-speed Protein Screening Using Transformer and ESM2

Simmering: Sufficient is better than optimal for training neural networks

PoisonBench: Assessing Large Language Model Vulnerability to Data Poisoning

Efficient Fine-Grained Guidance for Diffusion Model Based Symbolic Music Generation

AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML

VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters

Deconfounding Multi-Cause Latent Confounders: A Factor-Model Approach to Climate Model Bias Correction

DyGMamba: Efficiently Modeling Long-Term Temporal Dependency on Continuous-Time Dynamic Graphs with State Space Models

Proximal Policy Distillation

BoA: Attention-aware Post-training Quantization without Backpropagation

Certification for Differentially Private Prediction in Gradient-Based Training

Multi-Agent Collaboration via Cross-Team Orchestration

LlavaGuard: An Open VLM-based Framework for Safeguarding Vision Datasets and Models

Computational Limits of Low-Rank Adaptation (LoRA) Fine-Tuning for Transformer Models

Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network models

Mirage: A Multi-Level Superoptimizer for Tensor Programs

Multidimensional Adaptive Coefficient for Inference Trajectory Optimization in Flow and Diffusion

Longitudinal Targeted Minimum Loss-based Estimation with Temporal-Difference Heterogeneous Transformer

TMT: Tri-Modal Translation between Speech, Image, and Text by Processing Different Modalities as Different Languages

Structure Guided Large Language Model for SQL Generation

GraphGPT: Generative Pre-trained Graph Eulerian Transformer

Graph Deep Learning for Time Series Forecasting

Diffusion Policies for Out-of-Distribution Generalization in Offline Reinforcement Learning

Just Enough Thinking: Efficient Reasoning with Adaptive Length Penalties Reinforcement Learning

Rethinking Machine Unlearning in Image Generation Models

The Coming Crisis of Multi-Agent Misalignment: AI Alignment Must Be a Dynamic and Social Process

Improving Customer Service with Automatic Topic Detection in User Emails

Created by

Haebom

저자

Bojana Ba\v{s}aragin, Darija Medvecki, Gorana Gojic, Milena Oparnica, Dragi\v{s}a Mi\v{s}kovic

개요

본 논문은 세르비아의 주요 통신 회사인 Telekom Srbija에서 자동화된 이메일 주제 감지 및 라벨링을 통해 고객 서비스 효율성을 향상시키는 새로운 자연어 처리 파이프라인을 소개합니다. 이 파이프라인의 핵심은 비지도 토픽 모델링을 허용하는 모듈식 프레임워크인 BERTopic입니다. 일련의 전처리 및 후처리 단계를 거친 후, 수신 이메일에 12개의 주제 중 하나와 여러 추가 레이블을 할당하여 고객 서비스에서 맞춤형 애플리케이션을 통해 이메일을 필터링하고 액세스할 수 있도록 합니다. 세르비아어에 적용되었지만, 이 방법론은 개념적으로 언어에 구애받지 않으며, 특히 자원이 부족하고 형태소가 풍부한 다른 언어에도 쉽게 적용할 수 있습니다. 시스템 성능은 자동으로 할당된 주제의 속도와 정확성을 평가하여 평가되었으며, 이메일당 가중 평균 처리 시간은 0.041초, 가중 평균 F1 점수는 0.96입니다. 이 시스템은 현재 회사의 운영 환경에서 운영되며 자동화된 이메일 분류를 통해 고객 서비스 운영을 간소화합니다.

시사점, 한계점

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시사점:

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BERTopic을 활용한 효율적인 이메일 주제 자동 분류 시스템 구축 사례 제시.

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저자원 언어 및 형태소가 풍부한 언어에도 적용 가능한 방법론 제시.

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고객 서비스 효율성 향상 및 운영 간소화에 기여.

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높은 정확도 (가중 평균 F1 점수 0.96)와 빠른 처리 속도 (이메일당 0.041초) 달성.

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한계점:

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연구 대상이 특정 회사(Telekom Srbija)의 데이터에 한정됨.

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다른 언어나 도메인으로의 일반화 가능성에 대한 추가 연구 필요.

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BERTopic의 매개변수 조정 및 최적화에 대한 상세한 설명 부족.

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12개의 주제로의 분류가 모든 이메일 유형을 충분히 포괄하는지에 대한 검토 필요.

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