https://github.com/llm-lab-org/CLASP
Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
Papers with Code 아카이브
Retrieval-augmented Generation
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/lyqcom/mindspore_nlp_tutorial
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/marker-inc-korea/autorag
AutoRAG: Automated Framework for optimization of Retrieval Augmented Generation Pipeline
https://github.com/mem0ai/mem0
Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory
https://github.com/microsoft/graphrag
From Local to Global: A Graph RAG Approach to Query-Focused Summarization
https://github.com/mindspore-courses/d2l-mindspore/tree/master/chapter_15_natural_language_processing_applications
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/modelscope/mcpbench
Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation
https://github.com/muennighoff/sgpt
https://github.com/narabzad/llm-relevance-judgement-comparison
Initial Nugget Evaluation Results for the TREC 2024 RAG Track with the AutoNuggetizer Framework
https://github.com/novasearch-team/rag-retrieval
https://github.com/open-compass/opencompass
BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack
https://github.com/open-dataflow/rare
Benchmarking Retrieval-Augmented Generation in Multi-Modal Contexts
https://github.com/phbst/tinyrag
Learning to Filter Context for Retrieval-Augmented Generation
https://github.com/phunterlau/paper_without_code/tree/main/examples/community-rag
https://github.com/pku-aicare/colacare
https://github.com/prithivirajdamodaran/flashrank
COS-Mix: Cosine Similarity and Distance Fusion for Improved Information Retrieval
https://github.com/rachtibat/lrp-explains-transformers
https://github.com/rachtibat/lrp-for-transformers
https://github.com/rahcode7/semeval-shroom
Chainpoll: A high efficacy method for LLM hallucination detection
https://github.com/robotics-4-all/ISSEL-Announcements
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/salesforceairesearch/answer-engine-eval
Do RAG Systems Cover What Matters? Evaluating and Optimizing Responses with Sub-Question Coverage
https://github.com/ssundaram21/dreamsim
When Does Perceptual Alignment Benefit Vision Representations?
https://github.com/tginart/sglang
SGLang: Efficient Execution of Structured Language Model Programs
https://github.com/thu-keg/korc
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/unicamp-dl/retailGPT
Retail-GPT: leveraging Retrieval Augmented Generation (RAG) for building E-commerce Chat Assistants
https://github.com/vectara/FaithJudge
FaithBench: A Diverse Hallucination Benchmark for Summarization by Modern LLMs
https://github.com/vectara/hallucination-leaderboard
MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents
https://github.com/vividwalker/Multi-Head-Rag
https://github.com/yangyucheng000/University/tree/main/model-1/realm
https://github.com/yangyucheng000/University/tree/main/model-3/rag
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/yangyucheng000/University/tree/main/model-3/realm
https://github.com/yhzhu99/medagentboard
https://github.com/zhaoolee/garss
DeepRAG: Thinking to Retrieval Step by Step for Large Language Models
https://github.com/zhengxujosh/awesome-rag-vision
Retrieval Augmented Generation and Understanding in Vision: A Survey and New Outlook
https://huggingface.co/lion-ai/MedImageInsights
MedImageInsight: An Open-Source Embedding Model for General Domain Medical Imaging
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.