让RAG真正读懂“言外之意”！新框架引入词汇多样性，刷新多项基准SOTA

Core Insights - The article discusses the introduction of the Lexical Diversity-aware RAG (DRAG) framework, which enhances the accuracy of Retrieval-Augmented Generation (RAG) models by 10.6% and sets new state-of-the-art (SOTA) results in multiple benchmarks [1][2][16]. Group 1: Framework and Innovations - The DRAG framework systematically incorporates lexical diversity into the retrieval and generation processes of RAG, providing a lightweight, general, and easily extensible solution [1][5]. - The research team from Beihang University, Peking University, and Zhongguancun Laboratory highlights the importance of lexical diversity, which has been largely overlooked in existing RAG methods [4][5]. - Two key innovations are introduced: 1. Diversity-sensitive Relevance Analyzer (DRA), which dissects query semantics and employs differentiated strategies for various components, leading to a more granular relevance scoring [9]. 2. Risk-guided Sparse Calibration (RSC), which monitors the "misleading risk" of each generated token and calibrates decoding as necessary, ensuring the generation phase is not disturbed by irrelevant information [11][14]. Group 2: Performance and Results - The DRAG framework has shown significant performance improvements across various open-domain question-answering benchmarks, with notable accuracy increases in PopQA and TriviaQA by 4.9% and 4.4%, respectively, and a 10.6% increase in HotpotQA and 2WikiMultiHopQA [16]. - The method also outperforms existing models in long-answer generation metrics such as str-em and QA-F1, demonstrating strong generalization capabilities across different model sizes, including Llama2-7B and Llama2-13B [18][16]. Group 3: Lexical Diversity Challenges - The article identifies lexical diversity as a critical yet often neglected issue in RAG methods, where different expressions of the same question can confuse retrieval models, leading to incorrect answers [5][8]. - The framework addresses this by allowing semantic flexibility for variable components while ensuring strict matching for invariant components, thus improving the relevance of retrieved documents [12]. Group 4: Future Directions - The research team plans to expand the application of the DRAG framework to more specialized scenarios, aiming to enhance the understanding of complex human language expressions in large models [5].