Tag: Question Answering
All the articles with the tag "Question Answering".
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Direct Retrieval-augmented Optimization: Synergizing Knowledge Selection and Language Models
This paper introduces Direct Retrieval-augmented Optimization (DRO), a framework that synergizes knowledge selection and LLM generation through end-to-end training using a variational approach, achieving 5-15% improvements in EM and F1 scores across five QA datasets.
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LLM-Independent Adaptive RAG: Let the Question Speak for Itself
This paper introduces LLM-independent adaptive retrieval using 27 external information features across 7 groups, achieving comparable QA performance to LLM-based methods on 6 datasets while significantly improving efficiency by eliminating additional LLM calls during inference.
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SAGE: A Framework of Precise Retrieval for RAG
本文提出SAGE框架,通过语义分割、基于梯度的块选择和LLM自反馈机制,提高RAG系统的检索精度和问答性能,同时显著降低成本。