DMQR-RAG：透過多樣化多重查詢改寫提升檢索增強生成
為了解決大型語言模型在靜態知識和幻覺方面的局限性，本文提出了 DMQR-RAG，一個通用的多重查詢改寫框架，透過多樣化的改寫策略和自適應選擇方法，提升檢索增強生成中文件檢索和最終回應的效能。
DMQR-RAG: RAG를 위한 다양한 다중 쿼리 재작성
본 논문에서는 RAG에서 검색 성능과 응답 품질을 향상시키기 위해 다양한 재작성 쿼리를 활용하여 관련 문서의 재현율을 높이는 다중 쿼리 재작성 프레임워크인 DMQR-RAG를 제안합니다.
Improving Retrieval-Augmented Generation with Diverse Multi-Query Rewriting (DMQR-RAG)
DMQR-RAG improves the accuracy and relevance of retrieval-augmented generation (RAG) systems by employing diverse multi-query rewriting strategies to enhance the retrieval of relevant documents.
Calibrated Retrieval-Augmented Generation (CalibRAG) for Improved Decision-Making and Confidence Calibration in Large Language Models
CalibRAG, a novel retrieval method, enhances decision-making accuracy and confidence calibration in Large Language Models (LLMs) by integrating a forecasting function into the Retrieval-Augmented Generation (RAG) framework.
쿼리 확장 및 청크 그래프 재순위를 활용한 관광 도메인 RAG 기반 대규모 언어 모델 성능 향상 연구: QCG-Rerank 모델 제안
관광 도메인에서 간결한 사용자 쿼리와 방대한 데이터베이스 콘텐츠 간의 불일치 문제를 해결하기 위해 쿼리 확장 및 청크 그래프 재순위 기법을 활용하여 RAG 기반 대규모 언어 모델의 성능을 향상시키는 QCG-Rerank 모델을 제안한다.
Query Optimization for Parametric Knowledge Refinement in Retrieval-Augmented Large Language Models: Enhancing Accuracy and Utility in RAG Systems
The ERRR framework improves the accuracy and efficiency of Retrieval-Augmented Generation (RAG) systems by optimizing queries to refine the parametric knowledge of Large Language Models (LLMs).
邁向 RAG 的最佳搜尋與檢索：探討檢索準確度、雜訊和黃金文件召回率的影響
黃金文件的檢索對於 RAG 模型的效能至關重要，而降低近似最近鄰搜尋的準確度對效能的影響微乎其微，這為提高 RAG 模型的效率提供了可能性。
The Impact of Retrieval Optimization on Retrieval-Augmented Generation (RAG) for Question Answering
Optimizing the retrieval component of a Retrieval-Augmented Generation (RAG) pipeline, specifically focusing on gold document recall and approximate nearest neighbor search accuracy, significantly impacts the performance of downstream tasks like Question Answering (QA) and attributed QA.
Invar-RAG: A Novel Two-Stage Fine-Tuning Architecture for Retrieval-Augmented Generation Using a Single Large Language Model
Invar-RAG, a novel architecture for retrieval-augmented generation, leverages a single large language model (LLM) for both retrieval and generation, addressing limitations of traditional RAG systems by aligning representations and minimizing variance in retrieval to improve answer accuracy.
AssistRAG: An Intelligent Information Assistant to Improve Large Language Models for Complex Reasoning Tasks
AssistRAG is a novel framework that integrates an intelligent information assistant with Large Language Models (LLMs) to enhance their reasoning capabilities and address limitations of existing retrieval-augmented generation (RAG) methods.
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