$\texttt{MixGR}$: Enhancing Retriever Generalization for Scientific Domain through Complementary Granularity
- URL: http://arxiv.org/abs/2407.10691v1
- Date: Mon, 15 Jul 2024 13:04:09 GMT
- Title: $\texttt{MixGR}$: Enhancing Retriever Generalization for Scientific Domain through Complementary Granularity
- Authors: Fengyu Cai, Xinran Zhao, Tong Chen, Sihao Chen, Hongming Zhang, Iryna Gurevych, Heinz Koeppl,
- Abstract summary: This paper introduces $texttMixGR$, which improves dense retrievers' awareness of query-document matching across various levels of granularity.
$texttMixGR$ fuses various metrics based on these granularities to a united score that reflects a comprehensive query-document similarity.
Experiments demonstrate that $texttMixGR$ outperforms previous document retrieval by 24.7% and 9.8% on nDCG@5 with unsupervised and supervised retrievers.
- Score: 88.78750571970232
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent studies show the growing significance of document retrieval in the generation of LLMs, i.e., RAG, within the scientific domain by bridging their knowledge gap. However, dense retrievers often struggle with domain-specific retrieval and complex query-document relationships, particularly when query segments correspond to various parts of a document. To alleviate such prevalent challenges, this paper introduces $\texttt{MixGR}$, which improves dense retrievers' awareness of query-document matching across various levels of granularity in queries and documents using a zero-shot approach. $\texttt{MixGR}$ fuses various metrics based on these granularities to a united score that reflects a comprehensive query-document similarity. Our experiments demonstrate that $\texttt{MixGR}$ outperforms previous document retrieval by 24.7% and 9.8% on nDCG@5 with unsupervised and supervised retrievers, respectively, averaged on queries containing multiple subqueries from five scientific retrieval datasets. Moreover, the efficacy of two downstream scientific question-answering tasks highlights the advantage of $\texttt{MixGR}$to boost the application of LLMs in the scientific domain.
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