Papers with Code 아카이브

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/allen-li1231/treehop-rag

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TreeHop: Generate and Filter Next Query Embeddings Efficiently for Multi-hop Question Answering

https://github.com/allenai/ai2-scholarqa-lib

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Ai2 Scholar QA: Organized Literature Synthesis with Attribution

https://github.com/alphafin-proj/alphafin

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AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework

https://github.com/amaiya/onprem

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OnPrem.LLM: A Privacy-Conscious Document Intelligence Toolkit

https://github.com/amaze18/RAGbot-OpenSource-Comparison/blob/main/Hybrid_Topk_similarity_expt.ipynb

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Evaluating the Efficacy of Open-Source LLMs in Enterprise-Specific RAG Systems: A Comparative Study of Performance and Scalability

https://github.com/amazon-science/PrefEval

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Do LLMs Recognize Your Preferences? Evaluating Personalized Preference Following in LLMs

https://github.com/amazon-science/memerag

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MEMERAG: A Multilingual End-to-End Meta-Evaluation Benchmark for Retrieval Augmented Generation

https://github.com/amazon-science/neoqa

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NeoQA: Evidence-based Question Answering with Generated News Events

https://github.com/amazon-science/ragchecker

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RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation

https://github.com/ancientshi/erm4

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Enhancing Retrieval and Managing Retrieval: A Four-Module Synergy for Improved Quality and Efficiency in RAG Systems

https://github.com/andresalgaba/llm_citation_patterns

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Large Language Models Reflect Human Citation Patterns with a Heightened Citation Bias

https://github.com/aniquetahir/JORA

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JORA: JAX Tensor-Parallel LoRA Library for Retrieval Augmented Fine-Tuning

https://github.com/anlp-team/LTI_Neural_Navigator

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Enhancing LLM Factual Accuracy with RAG to Counter Hallucinations: A Case Study on Domain-Specific Queries in Private Knowledge-Bases

https://github.com/apple/ml-superposition-prompting

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Superposition Prompting: Improving and Accelerating Retrieval-Augmented Generation

https://github.com/applied-ai-research-lab/orchestrator-agent-trust

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Orchestrator-Agent Trust: A Modular Agentic AI Visual Classification System with Trust-Aware Orchestration and RAG-Based Reasoning

https://github.com/applied-machine-learning-lab/g3

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G3: An Effective and Adaptive Framework for Worldwide Geolocalization Using Large Multi-Modality Models

https://github.com/arash-shahmansoori/r2cbr3h-sr

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Concurrent Brainstorming & Hypothesis Satisfying: An Iterative Framework for Enhanced Retrieval-Augmented Generation (R2CBR3H-SR)

https://github.com/arghadippurdue/GraNNite

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GraNNite: Enabling High-Performance Execution of Graph Neural Networks on Resource-Constrained Neural Processing Units

https://github.com/artessay/sama

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TC-RAG:Turing-Complete RAG's Case study on Medical LLM Systems

https://github.com/arthur422tp/hierachical

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Tree-Based Text Retrieval via Hierarchical Clustering in RAGFrameworks: Application on Taiwanese Regulations

https://github.com/aryan-jadon/synthetic-data-generation-and-evaluation-using-reasoning-models

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Enhancing Domain-Specific Retrieval-Augmented Generation: Synthetic Data Generation and Evaluation using Reasoning Models

https://github.com/ashimdahal/study-of-convolutional-kolmogorov-arnold-networks

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Efficiency Bottlenecks of Convolutional Kolmogorov-Arnold Networks: A Comprehensive Scrutiny with ImageNet, AlexNet, LeNet and Tabular Classification

https://github.com/asinghcsu/agenticrag-survey

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Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG

https://github.com/asu-kim/personal-llm-kg

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Personalizing Large Language Models using Retrieval Augmented Generation and Knowledge Graph

https://github.com/atenanaz/poison-rag

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Poison-RAG: Adversarial Data Poisoning Attacks on Retrieval-Augmented Generation in Recommender Systems

https://github.com/augustinLib/SPIKE

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Imagine All The Relevance: Scenario-Profiled Indexing with Knowledge Expansion for Dense Retrieval

https://github.com/avsolatorio/gistembed

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GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning

https://github.com/awslabs/graphrag-toolkit

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Hierarchical Lexical Graph for Enhanced Multi-Hop Retrieval

https://github.com/awslabs/rag-qa-arena

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RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering

https://github.com/bai-lab/emotionalrag

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Emotional RAG: Enhancing Role-Playing Agents through Emotional Retrieval

https://github.com/bai-lab/kg-retriever

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KG-Retriever: Efficient Knowledge Indexing for Retrieval-Augmented Large Language Models

https://github.com/bayer-group/text-to-sql-epi-ehr-naacl2024

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Retrieval augmented text-to-SQL generation for epidemiological question answering using electronic health records

https://github.com/believewhat/JMLR-Joint-Medical-LLM-and-Retrieval-Training

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JMLR: Joint Medical LLM and Retrieval Training for Enhancing Reasoning and Professional Question Answering Capability

https://github.com/belindal/erase

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Language Modeling with Editable External Knowledge

https://github.com/betswish/mirage

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Model Internals-based Answer Attribution for Trustworthy Retrieval-Augmented Generation

https://github.com/biocypher/biochatter-light

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A Platform for the Biomedical Application of Large Language Models

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.