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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.18199 |
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| _version_ | 1866910232412684288 |
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| author | Terrenzi, Riccardo Falconi, Matteo Ayvaz, Serkan Plebani, Pierluigi |
| author_facet | Terrenzi, Riccardo Falconi, Matteo Ayvaz, Serkan Plebani, Pierluigi |
| contents | The rapid growth of tabular datasets in data lakes, data spaces, and open data portals makes effective dataset search essential for reuse and analysis. Existing search systems rely mainly on metadata, which is often incomplete or low quality, especially for tables whose meaning depends on both schema and cell values. Recent advances in Large Language Models (LLMs) enable richer, content-based representations of tables. However, prior LLM-based retrieval methods have focused on Table Question Answering, where the goal is to select a single table to answer a question, rather than retrieve and rank relevant datasets. We propose PIPER, a content-driven retrieval method for tabular datasets that uses table profiles and LLM-generated queries embedded for dense retrieval. Designed for dataset search in poor-metadata settings, PIPER outperforms both classical metadata-based baselines and strong TableQA retrieval methods, demonstrating the value of LLM-based content modeling for tabular dataset search. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_18199 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PIPER: Content-Based Table Search via profiling and LLM-Generated Pseudoqueries Terrenzi, Riccardo Falconi, Matteo Ayvaz, Serkan Plebani, Pierluigi Information Retrieval Artificial Intelligence The rapid growth of tabular datasets in data lakes, data spaces, and open data portals makes effective dataset search essential for reuse and analysis. Existing search systems rely mainly on metadata, which is often incomplete or low quality, especially for tables whose meaning depends on both schema and cell values. Recent advances in Large Language Models (LLMs) enable richer, content-based representations of tables. However, prior LLM-based retrieval methods have focused on Table Question Answering, where the goal is to select a single table to answer a question, rather than retrieve and rank relevant datasets. We propose PIPER, a content-driven retrieval method for tabular datasets that uses table profiles and LLM-generated queries embedded for dense retrieval. Designed for dataset search in poor-metadata settings, PIPER outperforms both classical metadata-based baselines and strong TableQA retrieval methods, demonstrating the value of LLM-based content modeling for tabular dataset search. |
| title | PIPER: Content-Based Table Search via profiling and LLM-Generated Pseudoqueries |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2605.18199 |