Retrieve Only Relevant Tables Whether Few or Many: Adaptive Table Retrieval Method
The article discusses a new adaptive table retrieval method designed to improve the retrieval of relevant tables from large databases based on natural language queries. This method addresses the limitations of traditional top-k retrieval strategies by adjusting the number of tables retrieved according to the specific needs of each query. Experimental results demonstrate that this approach enhances performance in both retrieval and downstream tasks.
- ▪The proposed method utilizes an adaptive thresholding mechanism to selectively retrieve tables.
- ▪A sliding-window reranking algorithm is integrated to efficiently process large table corpora.
- ▪Extensive experiments on datasets like Spider and BIRD show improved performance over existing methods.
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Computer Science > Information Retrieval arXiv:2605.18766 (cs) [Submitted on 12 Apr 2026] Title:Retrieve Only Relevant Tables Whether Few or Many: Adaptive Table Retrieval Method Authors:Taehee Kim, Seungbin Yang, Jihwan Kim, Jaegul Choo View a PDF of the paper titled Retrieve Only Relevant Tables Whether Few or Many: Adaptive Table Retrieval Method, by Taehee Kim and 3 other authors View PDF HTML (experimental) Abstract:Retrieving relevant tables from extensive databases for a given natural language query is essential for accurately answering questions in tasks such as text-to-SQL. Existing table retrieval approaches select a pre-determined set of k tables with the highest similarity to the query.
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