https://github.com/ocean-luna/hmrag
공식 코드HM-RAG: Hierarchical Multi-Agent Multimodal Retrieval Augmented Generation
Papers with Code 아카이브
Large Language Model
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/oceanntwt/ra-isf
공식 코드RA-ISF: Learning to Answer and Understand from Retrieval Augmentation via Iterative Self-Feedback
https://github.com/octaviaguo/EchoMist
공식 코드How to Protect Yourself from 5G Radiation? Investigating LLM Responses to Implicit Misinformation
https://github.com/oddqueue/e-dpo
공식 코드KL Penalty Control via Perturbation for Direct Preference Optimization
https://github.com/ofa-sys/expertllama
공식 코드ExpertPrompting: Instructing Large Language Models to be Distinguished Experts
https://github.com/om-ai-lab/OmAgent
공식 코드OmAgent: A Multi-modal Agent Framework for Complex Video Understanding with Task Divide-and-Conquer
https://github.com/om-ai-lab/ZoomEye
공식 코드https://github.com/om-ai-lab/imagerag
공식 코드ImageRAG: Enhancing Ultra High Resolution Remote Sensing Imagery Analysis with ImageRAG
https://github.com/omron-sinicx/whereistheanswer
공식 코드Where is the answer? Investigating Positional Bias in Language Model Knowledge Extraction
https://github.com/oneal2000/dragin
공식 코드https://github.com/oneal2000/judge
공식 코드JuDGE: Benchmarking Judgment Document Generation for Chinese Legal System
https://github.com/oneal2000/prag
공식 코드https://github.com/open-compass/CriticBench
공식 코드https://github.com/open-compass/criticeval
공식 코드https://github.com/open-social-world/egonormia
공식 코드https://github.com/openai/human-eval
공식 코드Safurai 001: New Qualitative Approach for Code LLM Evaluation
https://github.com/openai/whisper
공식 코드Adapting Large Language Model with Speech for Fully Formatted End-to-End Speech Recognition
https://github.com/openaudiolab/llast
공식 코드LLaST: Improved End-to-end Speech Translation System Leveraged by Large Language Models
https://github.com/openbiolink/thoughtsource
공식 코드ThoughtSource: A central hub for large language model reasoning data
https://github.com/openbmb/chatdev
공식 코드Scaling Large Language Model-based Multi-Agent Collaboration
https://github.com/openbmb/minicpm
공식 코드https://github.com/openbmb/rageval
공식 코드RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework
https://github.com/openbmb/repoagent
공식 코드RepoAgent: An LLM-Powered Open-Source Framework for Repository-level Code Documentation Generation
https://github.com/openbmb/ultrarag
공식 코드UltraRAG: A Modular and Automated Toolkit for Adaptive Retrieval-Augmented Generation
https://github.com/openbmb/viscpm
공식 코드Large Multilingual Models Pivot Zero-Shot Multimodal Learning across Languages
https://github.com/openbmb/visrag
공식 코드VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documents
https://github.com/opendatalab/OHR-Bench
공식 코드OCR Hinders RAG: Evaluating the Cascading Impact of OCR on Retrieval-Augmented Generation
https://github.com/opendatalab/OmniDocBench
공식 코드OmniDocBench: Benchmarking Diverse PDF Document Parsing with Comprehensive Annotations
https://github.com/opendatalab/openhueval
공식 코드OpenHuEval: Evaluating Large Language Model on Hungarian Specifics
https://github.com/opendfm/bai-scieval
공식 코드SciEval: A Multi-Level Large Language Model Evaluation Benchmark for Scientific Research
https://github.com/opendfm/rememberer
공식 코드Large Language Models Are Semi-Parametric Reinforcement Learning Agents
https://github.com/opendfm/scieval
공식 코드SciEval: A Multi-Level Large Language Model Evaluation Benchmark for Scientific Research
https://github.com/opengvlab/diffagent
공식 코드DiffAgent: Fast and Accurate Text-to-Image API Selection with Large Language Model
https://github.com/opengvlab/instruct2act
공식 코드Instruct2Act: Mapping Multi-modality Instructions to Robotic Actions with Large Language Model
https://github.com/opengvlab/interngpt
공식 코드VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Tasks
https://github.com/opengvlab/internvl
공식 코드InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.