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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/congressRA/sample-agent

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Agent-Enhanced Large Language Models for Researching Political Institutions

https://github.com/contextualai/gritlm

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Generative Representational Instruction Tuning

https://github.com/copenlu/context-utilisation-for-rag

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A Reality Check on Context Utilisation for Retrieval-Augmented Generation

https://github.com/copenlu/dynamicqa

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DYNAMICQA: Tracing Internal Knowledge Conflicts in Language Models

https://github.com/crealabs/enhanced-bge-m3-with-clp-and-moe

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Efficient fine-tuning methodology of text embedding models for information retrieval: contrastive learning penalty (clp)

https://github.com/cs-edwards/advrag

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Hybrid Context Retrieval Augmented Generation Pipeline: LLM-Augmented Knowledge Graphs and Vector Database for Accreditation Reporting Assistance

https://github.com/cshaitao/lexrag

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LexRAG: Benchmarking Retrieval-Augmented Generation in Multi-Turn Legal Consultation Conversation

https://github.com/cssai-research/muserag

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MuseRAG: Idea Originality Scoring At Scale

https://github.com/cv-lee/financerag

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Multi-Reranker: Maximizing performance of retrieval-augmented generation in the FinanceRAG challenge

https://github.com/cxcscmu/ragviz

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RAGViz: Diagnose and Visualize Retrieval-Augmented Generation

https://github.com/cyberooo/manicod

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Detecting Manipulated Contents Using Knowledge-Grounded Inference

https://github.com/dair-iitd/flonet

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End-to-End Learning of Flowchart Grounded Task-Oriented Dialogs

https://github.com/dangxingyu/rnn-icrag

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RNNs are not Transformers (Yet): The Key Bottleneck on In-context Retrieval

https://github.com/danielkty/debiasing-rag

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Mitigating Bias in RAG: Controlling the Embedder

https://github.com/daod/spring

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One Token Can Help! Learning Scalable and Pluggable Virtual Tokens for Retrieval-Augmented Large Language Models

https://github.com/datarobot/syftr

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syftr: Pareto-Optimal Generative AI

https://github.com/davidabrahamyan/stackrag

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StackRAG Agent: Improving Developer Answers with Retrieval-Augmented Generation

https://github.com/declare-lab/trust-align

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Measuring and Enhancing Trustworthiness of LLMs in RAG through Grounded Attributions and Learning to Refuse

https://github.com/deep-polyu/awesome-graphrag

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A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models

https://github.com/deeplearnxmu/faithful-rag

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FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation

https://github.com/deperiaskerre/qkg

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Semantic Enrichment of the Quantum Cascade Laser Properties in Text- A Knowledge Graph Generation Approach

https://github.com/difeigu/radalign

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RadAlign: Advancing Radiology Report Generation with Vision-Language Concept Alignment

https://github.com/digitalharborfoundation/rag-for-math-qa

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Retrieval-augmented Generation to Improve Math Question-Answering: Trade-offs Between Groundedness and Human Preference

https://github.com/dineshsrivasthav/adaptive_meta_learning_with_multi_agent_framework

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Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization

https://github.com/disi-unibo-nlp/rag-when-how-chunk

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Reconstructing Context: Evaluating Advanced Chunking Strategies for Retrieval-Augmented Generation

https://github.com/dlawjddn803/info

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You Truly Understand What I Need: Intellectual and Friendly Dialogue Agents grounding Knowledge and Persona

https://github.com/dmis-lab/compact

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CompAct: Compressing Retrieved Documents Actively for Question Answering

https://github.com/dmis-lab/rag2

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Rationale-Guided Retrieval Augmented Generation for Medical Question Answering

https://github.com/dmis-lab/self-biorag

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Improving Medical Reasoning through Retrieval and Self-Reflection with Retrieval-Augmented Large Language Models

https://github.com/docailab/xrag

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XRAG: eXamining the Core -- Benchmarking Foundational Components in Advanced Retrieval-Augmented Generation

https://github.com/dongguanting/FollowRAG

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Toward General Instruction-Following Alignment for Retrieval-Augmented Generation

https://github.com/dongguanting/dpa-rag

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Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generation

https://github.com/dosonleung/fasttog

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Fast Think-on-Graph: Wider, Deeper and Faster Reasoning of Large Language Model on Knowledge Graph

https://github.com/dreammr/rap

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Retrieval-Augmented Perception: High-Resolution Image Perception Meets Visual RAG

https://github.com/drusso98/face-the-facts

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Face the Facts! Evaluating RAG-based Fact-checking Pipelines in Realistic Settings

https://github.com/dsg-polymtl/flockmtl

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Beyond Quacking: Deep Integration of Language Models and RAG into DuckDB

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