https://github.com/alickzhu/player
공식 코드PLAYER*: Enhancing LLM-based Multi-Agent Communication and Interaction in Murder Mystery Games
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PLAYER*: Enhancing LLM-based Multi-Agent Communication and Interaction in Murder Mystery Games
PEER: Expertizing Domain-Specific Tasks with a Multi-Agent Framework and Tuning Methods
Evaluating Retrieval Quality in Retrieval-Augmented Generation
Towards Neural Schema Alignment for OpenStreetMap and Knowledge Graphs
Leveraging Training Data in Few-Shot Prompting for Numerical Reasoning
Refiner: Restructure Retrieval Content Efficiently to Advance Question-Answering Capabilities
TreeHop: Generate and Filter Next Query Embeddings Efficiently for Multi-hop Question Answering
Holodeck: Language Guided Generation of 3D Embodied AI Environments
Exploiting Explicit Paths for Multi-hop Reading Comprehension
COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs
UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark
What Does My QA Model Know? Devising Controlled Probes using Expert Knowledge
Revealing Weaknesses in Text Watermarking Through Self-Information Rewrite Attacks
AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework
Automatic benchmarking of large multimodal models via iterative experiment programming
Leveraging LLM Embeddings for Cross Dataset Label Alignment and Zero Shot Music Emotion Prediction
JamendoMaxCaps: A Large Scale Music-caption Dataset with Imputed Metadata
SonicVerse: Multi-Task Learning for Music Feature-Informed Captioning
PiFlow: Principle-aware Scientific Discovery with Multi-Agent Collaboration
OnPrem.LLM: A Privacy-Conscious Document Intelligence Toolkit
GenIC: An LLM-Based Framework for Instance Completion in Knowledge Graphs
Self-Supervised Hyperboloid Representations from Logical Queries over Knowledge Graphs
Progressive Coordinate Transforms for Monocular 3D Object Detection
Instilling Type Knowledge in Language Models via Multi-Task QA
Automated Evaluation of Retrieval-Augmented Language Models with Task-Specific Exam Generation
Large Language Model Critics for Execution-Free Evaluation of Code Changes
MEMERAG: A Multilingual End-to-End Meta-Evaluation Benchmark for Retrieval Augmented Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.