{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T19:48:32Z","timestamp":1774986512157,"version":"3.50.1"},"reference-count":64,"publisher":"Association for Computing Machinery (ACM)","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. ACM Manag. Data"],"published-print":{"date-parts":[[2025,6,17]]},"abstract":"<jats:p>Finding relevant tables among databases, lakes, and repositories is the first step in extracting value from data. Such a task remains difficult because assessing whether a table is relevant to a problem does not always depend only on its content but also on the context, which is usually tribal knowledge known to the individual or team. While tools like data catalogs and academic data discovery systems target this problem, they rely on keyword search or more complex interfaces, limiting non-technical users' ability to find relevant data. The advent of large language models (LLMs) offers a unique opportunity for users to ask questions directly in natural language, making dataset discovery more intuitive, accessible, and efficient.<\/jats:p>\n                  <jats:p>\n                    In this paper, we introduce\n                    <jats:sc>Pneuma<\/jats:sc>\n                    , a retrieval-augmented generation (RAG) system designed to efficiently and effectively discover tabular data.\n                    <jats:sc>Pneuma<\/jats:sc>\n                    leverages large language models (LLMs) for both table representation and table retrieval. For table representation,\n                    <jats:sc>Pneuma<\/jats:sc>\n                    preserves schema and row-level information to ensure comprehensive data understanding. For table retrieval,\n                    <jats:sc>Pneuma<\/jats:sc>\n                    augments LLMs with traditional information retrieval techniques, such as full-text and vector search, harnessing the strengths of both to improve retrieval performance. To evaluate\n                    <jats:sc>Pneuma<\/jats:sc>\n                    , we generate comprehensive benchmarks that simulate table discovery workload on six real-world datasets including enterprise data, scientific databases, warehousing data, and open data. Our results demonstrate that\n                    <jats:sc>Pneuma<\/jats:sc>\n                    outperforms widely used table search systems (such as full-text search and state-of-the-art RAG systems) in accuracy and resource efficiency.\n                  <\/jats:p>","DOI":"10.1145\/3725337","type":"journal-article","created":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T21:23:29Z","timestamp":1750281809000},"page":"1-28","source":"Crossref","is-referenced-by-count":3,"title":["<scp>Pneuma<\/scp>\n                    : Leveraging LLMs for Tabular Data Representation and Retrieval in an End-to-End System"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-5324-7758","authenticated-orcid":false,"given":"Muhammad Imam Luthfi","family":"Balaka","sequence":"first","affiliation":[{"name":"University of Indonesia, Depok, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6239-9092","authenticated-orcid":false,"given":"David","family":"Alexander","sequence":"additional","affiliation":[{"name":"University of Indonesia, Depok, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6554-4161","authenticated-orcid":false,"given":"Qiming","family":"Wang","sequence":"additional","affiliation":[{"name":"The University of Chicago, Chicago, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8646-473X","authenticated-orcid":false,"given":"Yue","family":"Gong","sequence":"additional","affiliation":[{"name":"The University of Chicago, Chicago, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0745-6804","authenticated-orcid":false,"given":"Adila","family":"Krisnadhi","sequence":"additional","affiliation":[{"name":"University of Indonesia, Depok, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7675-6080","authenticated-orcid":false,"given":"Raul","family":"Castro Fernandez","sequence":"additional","affiliation":[{"name":"The University of Chicago, Chicago, USA"}]}],"member":"320","published-online":{"date-parts":[[2025,6,18]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al.","author":"Achiam Josh","year":"2023","unstructured":"Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al., 2023. 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