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/idea-finai/tog-2

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Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation

https://github.com/ighina/llmclimate2024

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Efficient Aspect-Based Summarization of Climate Change Reports with Small Language Models

https://github.com/ignorejjj/longrefiner

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Hierarchical Document Refinement for Long-context Retrieval-augmented Generation

https://github.com/ignorejjj/metarag

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Metacognitive Retrieval-Augmented Large Language Models

https://github.com/illuin-tech/grouse

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GroUSE: A Benchmark to Evaluate Evaluators in Grounded Question Answering

https://github.com/imenjaoua/hybrid-code-review

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Combining Large Language Models with Static Analyzers for Code Review Generation

https://github.com/imran2205/logicrag

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Logic-RAG: Augmenting Large Multimodal Models with Visual-Spatial Knowledge for Road Scene Understanding

https://github.com/intellabs/rag-retrieval-study

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Toward Optimal Search and Retrieval for RAG

https://github.com/intellisensing/rs-agent

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RS-Agent: Automating Remote Sensing Tasks through Intelligent Agent

https://github.com/intitni/copilotforxcode

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Contextual Augmented Multi-Model Programming (CAMP): A Hybrid Local-Cloud Copilot Framework

https://github.com/intuit-ai-research/knowledge-infused-ai/tree/main/synthetic-knowledge-ingestion

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Synthetic Knowledge Ingestion: Towards Knowledge Refinement and Injection for Enhancing Large Language Models

https://github.com/ioannis-papadimitriou/rag-playground

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RAG Playground: A Framework for Systematic Evaluation of Retrieval Strategies and Prompt Engineering in RAG Systems

https://github.com/ipouyall/can-llms-be-lateral-thinkers

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uTeBC-NLP at SemEval-2024 Task 9: Can LLMs be Lateral Thinkers?

https://github.com/iqvianlp/ontologyrag

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OntologyRAG: Better and Faster Biomedical Code Mapping with Retrieval-Augmented Generation (RAG) Leveraging Ontology Knowledge Graphs and Large Language Models

https://github.com/irgroup/Reanimator

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REANIMATOR: Reanimate Retrieval Test Collections with Extracted and Synthetic Resources

https://github.com/irlabamsterdam/cone-rag

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Conversational Gold: Evaluating Personalized Conversational Search System using Gold Nuggets

https://github.com/isaacwasserman/LongRAG

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Enhancing Retrieval in QA Systems with Derived Feature Association

https://github.com/ist-daslab/panzamail

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Panza: Design and Analysis of a Fully-Local Personalized Text Writing Assistant

https://github.com/jasaxion/irsc_benchmark

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IRSC: A Zero-shot Evaluation Benchmark for Information Retrieval through Semantic Comprehension in Retrieval-Augmented Generation Scenarios

https://github.com/jerrywu-code/susgen

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SusGen-GPT: A Data-Centric LLM for Financial NLP and Sustainability Report Generation

https://github.com/jianxu95/rallrec

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RALLRec: Improving Retrieval Augmented Large Language Model Recommendation with Representation Learning

https://github.com/jihyechoi77/malade

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MALADE: Orchestration of LLM-powered Agents with Retrieval Augmented Generation for Pharmacovigilance

https://github.com/jimengshi/hypercube-rag

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Hypercube-RAG: Hypercube-Based Retrieval-Augmented Generation for In-domain Scientific Question-Answering

https://github.com/jinghanzhang1998/ratt

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RATT: A Thought Structure for Coherent and Correct LLM Reasoning

https://github.com/jiny1623/dynamicer

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DynamicER: Resolving Emerging Mentions to Dynamic Entities for RAG

https://github.com/jinyangwu/NoiserBench

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Pandora's Box or Aladdin's Lamp: A Comprehensive Analysis Revealing the Role of RAG Noise in Large Language Models

https://github.com/jinyansu1/aggd

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Corpus Poisoning via Approximate Greedy Gradient Descent

https://github.com/jinyansu1/eval_poisonrag

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Towards More Robust Retrieval-Augmented Generation: Evaluating RAG Under Adversarial Poisoning Attacks

https://github.com/jinyansu1/flare-aug

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Fast or Better? Balancing Accuracy and Cost in Retrieval-Augmented Generation with Flexible User Control

https://github.com/jinzhuoran/rag-rewardbench

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RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment

https://github.com/jlab-nlp/retrieval-augmented-correction

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RAC: Efficient LLM Factuality Correction with Retrieval Augmentation

https://github.com/jmnian/weak_label_for_rag

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W-RAG: Weakly Supervised Dense Retrieval in RAG for Open-domain Question Answering

https://github.com/jmnian/wrag

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W-RAG: Weakly Supervised Dense Retrieval in RAG for Open-domain Question Answering

https://github.com/john-ai-lab/rapid

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Long-Context Inference with Retrieval-Augmented Speculative Decoding

https://github.com/jonlin223/raccoon-geocoding

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RACCOON: A Retrieval-Augmented Generation Approach for Location Coordinate Capture from News Articles

https://github.com/jou2024/EXPRAG

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Experience Retrieval-Augmentation with Electronic Health Records Enables Accurate Discharge QA

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