https://github.com/WPENGxs/X-WebAgentBench
공식 코드X-WebAgentBench: A Multilingual Interactive Web Benchmark for Evaluating Global Agentic System
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
X-WebAgentBench: A Multilingual Interactive Web Benchmark for Evaluating Global Agentic System
Plug-and-Play Performance Estimation for LLM Services without Relying on Labeled Data
STeCa: Step-level Trajectory Calibration for LLM Agent Learning
Enhancing High-order Interaction Awareness in LLM-based Recommender Model
Real-time Animation Generation and Control on Rigged Models via Large Language Models
RewardAnything: Generalizable Principle-Following Reward Models
DRCap: Decoding CLAP Latents with Retrieval-Augmented Generation for Zero-shot Audio Captioning
mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration
UniPoll: A Unified Social Media Poll Generation Framework via Multi-Objective Optimization
A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQL
LLM-RankFusion: Mitigating Intrinsic Inconsistency in LLM-based Ranking
Towards Joint Modeling of Dialogue Response and Speech Synthesis based on Large Language Model
SimGRAG: Leveraging Similar Subgraphs for Knowledge Graphs Driven Retrieval-Augmented Generation
Generating Origin-Destination Matrices in Neural Spatial Interaction Models
CacheBlend: Fast Large Language Model Serving for RAG with Cached Knowledge Fusion
Challenges of Linking Organizational Information in Open Government Data to Knowledge Graphs
Openstory++: A Large-scale Dataset and Benchmark for Instance-aware Open-domain Visual Storytelling
Predicting ConceptNet Path Quality Using Crowdsourced Assessments of Naturalness
Mind Scramble: Unveiling Large Language Model Psychology Via Typoglycemia
EQ-Negotiator: An Emotion-Reasoning LLM Agent in Credit Dialogues
LLM-TabFlow: Synthetic Tabular Data Generation with Inter-column Logical Relationship Preservation
Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games
KARMA: Leveraging Multi-Agent LLMs for Automated Knowledge Graph Enrichment
Mix-of-Granularity: Optimize the Chunking Granularity for Retrieval-Augmented Generation
L-CiteEval: Do Long-Context Models Truly Leverage Context for Responding?
MotionLLaMA: A Unified Framework for Motion Synthesis and Comprehension
Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.