https://github.com/adamlerer/synthwiki
Attention Sorting Combats Recency Bias In Long Context Language Models
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/alextakele/python-random-quote
RAGAS: Automated Evaluation of Retrieval Augmented Generation
https://github.com/ali7naseh/RAG_MIA
Riddle Me This! Stealthy Membership Inference for Retrieval-Augmented Generation
https://github.com/alibaba-nlp/webagent
https://github.com/andypinxinliu/GestureLSM
Retrieving Semantics from the Deep: an RAG Solution for Gesture Synthesis
https://github.com/artessay/ragraph
TC-RAG:Turing-Complete RAG's Case study on Medical LLM Systems
https://github.com/assafelovic/gpt-researcher
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/bangoc123/drop-rag
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/bangoc123/retrieval-backend-with-rag
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/castorini/nuggetizer
Ragnarök: A Reusable RAG Framework and Baselines for TREC 2024 Retrieval-Augmented Generation Track
https://github.com/castorini/rank_llm
https://github.com/chancefocus/pixiu
FinBen: A Holistic Financial Benchmark for Large Language Models
https://github.com/circlemind-ai/fast-graphrag
HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models
https://github.com/codelion/adaptive-classifier
LettuceDetect: A Hallucination Detection Framework for RAG Applications
https://github.com/codelion/optillm/blob/main/scripts/eval_frames_benchmark.py
Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation
https://github.com/cwida/pdx
AiSAQ: All-in-Storage ANNS with Product Quantization for DRAM-free Information Retrieval
https://github.com/cybermetric/cybermetric
https://github.com/danny911kr/REALTALK
Evaluating Very Long-Term Conversational Memory of LLM Agents
https://github.com/data-prep-kit/data-prep-kit
Data-Prep-Kit: getting your data ready for LLM application development
https://github.com/deepset-ai/haystack
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/docarray/docarray
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/emory-irlab/argumentation-rag
ConQRet: Benchmarking Fine-Grained Evaluation of Retrieval Augmented Argumentation with LLM Judges
https://github.com/explodinggradients/ragas
RAGAS: Automated Evaluation of Retrieval Augmented Generation
https://github.com/gusye1234/nano-graphrag
From Local to Global: A Graph RAG Approach to Query-Focused Summarization
https://github.com/hkuds/auto-deep-research
AutoAgent: A Fully-Automated and Zero-Code Framework for LLM Agents
https://github.com/hzy312/knowledge-r1
Reinforced Internal-External Knowledge Synergistic Reasoning for Efficient Adaptive Search Agent
https://github.com/ibm-self-serve-assets/metagen-blended-rag
MetaGen Blended RAG: Higher Accuracy for Domain-Specific Q&A Without Fine-Tuning
https://github.com/illuin-tech/colpali
ColPali: Efficient Document Retrieval with Vision Language Models
https://github.com/illuin-tech/vidore-benchmark
ColPali: Efficient Document Retrieval with Vision Language Models
https://github.com/inclusionai/aworld
RAG-R1 : Incentivize the Search and Reasoning Capabilities of LLMs through Multi-query Parallelism
https://github.com/inspire-group/RobustRAG
https://github.com/internlm/huixiangdou
https://github.com/kaistai/semiparametric_token-sequence_co-supervision
Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
https://github.com/kevinwu23/SourceCheckup
How well do LLMs cite relevant medical references? An evaluation framework and analyses
https://github.com/lastmile-ai/aiconfig/tree/main/cookbooks/Chain-of-Verification
A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models
https://github.com/levon003/wiki-ores-feedback
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.