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。
본 논문에서는 RAG에서 검색 성능과 응답 품질을 향상시키기 위해 다양한 재작성 쿼리를 활용하여 관련 문서의 재현율을 높이는 다중 쿼리 재작성 프레임워크인 DMQR-RAG를 제안합니다.
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.
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.
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.
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).
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。
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 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 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.