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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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.18766 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-20T04:04:59.484Z |
| Last seen | 2026-05-20T04:04:59.484Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | Gf904fLlA5G2 |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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