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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/hkuds/videorag

공식 코드

VideoRAG: Retrieval-Augmented Generation with Extreme Long-Context Videos

https://github.com/hkust-knowcomp/privatengdb

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Privacy-Preserved Neural Graph Databases

https://github.com/holistic-ai/THaMES

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THaMES: An End-to-End Tool for Hallucination Mitigation and Evaluation in Large Language Models

https://github.com/hoorangyee/lrage

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LARGE: Legal Retrieval Augmented Generation Evaluation Tool

https://github.com/howiehwong/unigen

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UniGen: A Unified Framework for Textual Dataset Generation Using Large Language Models

https://github.com/hsliu-initial/ctrla

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CtrlA: Adaptive Retrieval-Augmented Generation via Inherent Control

https://github.com/hsm316/llm_wikipedia

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Wikipedia in the Era of LLMs: Evolution and Risks

https://github.com/huangl22/chat3gpp

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Chat3GPP: An Open-Source Retrieval-Augmented Generation Framework for 3GPP Documents

https://github.com/huenao/debate-augmented-rag

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Removal of Hallucination on Hallucination: Debate-Augmented RAG

https://github.com/huggingface/transformers

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Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering

https://github.com/hughvd/um-cai-fellowship

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From Interests to Insights: An LLM Approach to Course Recommendations Using Natural Language Queries

https://github.com/huichizhou/trustrag

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TrustRAG: Enhancing Robustness and Trustworthiness in RAG

https://github.com/huiminzeng/gpt-fedrec

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Federated Recommendation via Hybrid Retrieval Augmented Generation

https://github.com/huskyinsalt/crag

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Corrective Retrieval Augmented Generation

https://github.com/hwy9855/gsr

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Less is More: Making Smaller Language Models Competent Subgraph Retrievers for Multi-hop KGQA

https://github.com/hyeonjeongha/mm-poisonrag

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MM-PoisonRAG: Disrupting Multimodal RAG with Local and Global Poisoning Attacks

https://github.com/hyintell/retrievalqa

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RetrievalQA: Assessing Adaptive Retrieval-Augmented Generation for Short-form Open-Domain Question Answering

https://github.com/hymie122/rag-survey

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Retrieval-Augmented Generation for AI-Generated Content: A Survey

https://github.com/iaar-shanghai/crud_rag

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CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models

https://github.com/iaar-shanghai/pgrag

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Empowering Large Language Models to Set up a Knowledge Retrieval Indexer via Self-Learning

https://github.com/iaar-shanghai/saferag

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SafeRAG: Benchmarking Security in Retrieval-Augmented Generation of Large Language Model

https://github.com/iai-group/ginger-response-generation

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GINGER: Grounded Information Nugget-Based Generation of Responses

https://github.com/ibm-ecosystem-engineering/blended-rag

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Blended RAG: Improving RAG (Retriever-Augmented Generation) Accuracy with Semantic Search and Hybrid Query-Based Retrievers

https://github.com/ibm-granite/granite-guardian

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Granite Guardian

https://github.com/ibm/data-prep-kit

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Data-Prep-Kit: getting your data ready for LLM application development

https://github.com/ibm/kgi-slot-filling

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Robust Retrieval Augmented Generation for Zero-shot Slot Filling

https://github.com/ibm/mt-rag-benchmark

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MTRAG: A Multi-Turn Conversational Benchmark for Evaluating Retrieval-Augmented Generation Systems

https://github.com/icip-cas/selfretrieval

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Self-Retrieval: End-to-End Information Retrieval with One Large Language Model

https://github.com/ict-bigdatalab/rc-rag

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Controlling Risk of Retrieval-augmented Generation: A Counterfactual Prompting Framework

https://github.com/ict-bigdatalab/right

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RIGHT: Retrieval-augmented Generation for Mainstream Hashtag Recommendation

https://github.com/ict-bigdatalab/utility_judgments

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Are Large Language Models Good at Utility Judgments?

https://github.com/ictnlp/LevelRAG

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LevelRAG: Enhancing Retrieval-Augmented Generation with Multi-hop Logic Planning over Rewriting Augmented Searchers

https://github.com/ictnlp/auto-rag

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Auto-RAG: Autonomous Retrieval-Augmented Generation for Large Language Models

https://github.com/ictnlp/flexrag

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FlexRAG: A Flexible and Comprehensive Framework for Retrieval-Augmented Generation

https://github.com/idea-finai/ragllava

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MLLM Is a Strong Reranker: Advancing Multimodal Retrieval-augmented Generation via Knowledge-enhanced Reranking and Noise-injected Training

https://github.com/idea-finai/ragvl

공식 코드

MLLM Is a Strong Reranker: Advancing Multimodal Retrieval-augmented Generation via Knowledge-enhanced Reranking and Noise-injected Training

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