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Retrieval-augmented Generation
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/microsoft/KBLaM
공식 코드https://github.com/microsoft/pike-rag
공식 코드PIKE-RAG: sPecIalized KnowledgE and Rationale Augmented Generation
https://github.com/microsoft/rebel
공식 코드https://github.com/microsoft/sammo
공식 코드https://github.com/mikelixiang88/context-matters
공식 코드https://github.com/mindverse/rag-tuned-llm
공식 코드https://github.com/mk322/ever_hallu
공식 코드https://github.com/mllmkcbench/mllmkc
공식 코드Benchmarking Multimodal Knowledge Conflict for Large Multimodal Models
https://github.com/mmdocrag/mmdocrag
공식 코드Benchmarking Retrieval-Augmented Multimomal Generation for Document Question Answering
https://github.com/monarch-initiative/dragon-ai-results
공식 코드Dynamic Retrieval Augmented Generation of Ontologies using Artificial Intelligence (DRAGON-AI)
https://github.com/monarch-initiative/gpt-ontology-completion-analysis
공식 코드Dynamic Retrieval Augmented Generation of Ontologies using Artificial Intelligence (DRAGON-AI)
https://github.com/mozerwang/loong
공식 코드Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA
https://github.com/mramg-bench/mramg
공식 코드https://github.com/mtkresearch/rad-bench
공식 코드RAD-Bench: Evaluating Large Language Models Capabilities in Retrieval Augmented Dialogues
https://github.com/muktac5/ciir-liverag
공식 코드CIIR@LiveRAG 2025: Optimizing Multi-Agent Retrieval Augmented Generation through Self-Training
https://github.com/mxpoliakov/multi-meta-rag
공식 코드https://github.com/my-ai-university/finite-element-method
공식 코드AI-University: An LLM-based platform for instructional alignment to scientific classrooms
https://github.com/myazann/ap-bots
공식 코드Improving RAG for Personalization with Author Features and Contrastive Examples
https://github.com/myeon9h/planrag
공식 코드https://github.com/n4feng/ResponseQualityAssessment
공식 코드Response Quality Assessment for Retrieval-Augmented Generation via Conditional Conformal Factuality
https://github.com/naist-nlp/CC_RAG
공식 코드https://github.com/nan-huo/micro-act
공식 코드Micro-Act: Mitigate Knowledge Conflict in Question Answering via Actionable Self-Reasoning
https://github.com/narabzad/genir-evaluation
공식 코드https://github.com/narabzad/prompt-sensitivity-relevance-judgements
공식 코드A Human-AI Comparative Analysis of Prompt Sensitivity in LLM-Based Relevance Judgment
https://github.com/naver/bergen
공식 코드BERGEN: A Benchmarking Library for Retrieval-Augmented Generation
https://github.com/ncsulsj/robust_sumsystem
공식 코드https://github.com/netop-team/telco-rag
공식 코드Telco-RAG: Navigating the Challenges of Retrieval-Augmented Language Models for Telecommunications
https://github.com/neuir/m2rag
공식 코드Benchmarking Retrieval-Augmented Generation in Multi-Modal Contexts
https://github.com/neuir/rankcot
공식 코드RankCoT: Refining Knowledge for Retrieval-Augmented Generation through Ranking Chain-of-Thoughts
https://github.com/neulab/ragged
공식 코드RAGGED: Towards Informed Design of Retrieval Augmented Generation Systems
https://github.com/nicerwang/joint-gcg
공식 코드Joint-GCG: Unified Gradient-Based Poisoning Attacks on Retrieval-Augmented Generation Systems
https://github.com/nick-zheng-q/hdlxgraph
공식 코드HDLxGraph: Bridging Large Language Models and HDL Repositories via HDL Graph Databases
https://github.com/nicpopovic/ember
공식 코드https://github.com/nicpopovic/stoke
공식 코드https://github.com/nikolamilosevic86/verif.ai
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.