{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T03:18:53Z","timestamp":1758079133154,"version":"3.44.0"},"reference-count":22,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:p>\n            Recent breakthroughs in artificial intelligence have produced\n            <jats:italic toggle=\"yes\">Large Language Models<\/jats:italic>\n            (LLMs) and a new wave of\n            <jats:italic toggle=\"yes\">Tabular Foundation Models<\/jats:italic>\n            (TFMs). Both promise to redefine how we query, integrate, and reason over relational data, yet they embody opposing philosophies: LLMs pursue broad generality through massive text-centric pre-training, whereas TFMs embed inductive biases that mirror table structure and relational semantics. This panel assembles researchers and practitioners from academia and industry to debate which path, specialized TFMs, ever stronger general-purpose LLMs, or a hybrid of the two, will most effectively power the next generation of data management systems. Panelists will confront questions of generality, accuracy, scalability, robustness, cost, and usability across core data management tasks such as Text-to-SQL translation, schema understanding, and entity resolution. The discussion aims to surface critical research challenges and guide the community's investment of effort and resources over the coming years.\n          <\/jats:p>","DOI":"10.14778\/3750601.3760519","type":"journal-article","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T13:38:05Z","timestamp":1758029885000},"page":"5513-5515","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Panel on Neural Relational Data: Tabular Foundation Models, LLMs... or Both?"],"prefix":"10.14778","volume":"18","author":[{"given":"Paolo","family":"Papotti","sequence":"first","affiliation":[{"name":"EURECOM"}]},{"given":"Carsten","family":"Binnig","sequence":"additional","affiliation":[{"name":"TU Darmstadt"}]}],"member":"320","published-online":{"date-parts":[[2025,9,16]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00544"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-25007-6_25"},{"key":"e_1_2_1_3_1","volume-title":"NeurIPS 2022 First Table Representation Workshop.","author":"Cahoon Joyce","year":"2022","unstructured":"Joyce Cahoon, Alexandra Savelieva, Andreas C Mueller, Avrilia Floratou, Carlo Curino, Hiren Patel, Jordan Henkel, Markus Weimer, Nellie Gustafsson, Richard Wydrowski, et al. 2022. 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Large Language Models on Tabular Data-A Survey. arXiv preprint arXiv:2402.17944 (2024)."},{"key":"e_1_2_1_8_1","volume-title":"Brandon Chow, Kai Deng, Katherine Lin, Marcos Campos, K. Venkatesh Emani, Vivek Pandit, Victor Shnayder, Wenjing Wang, and Carlo Curino.","author":"Floratou Avrilia","year":"2024","unstructured":"Avrilia Floratou, Fotis Psallidas, Fuheng Zhao, Shaleen Deep, Gunther Hagleither, Wangda Tan, Joyce Cahoon, Rana Alotaibi, Jordan Henkel, Abhik Singla, Alex Van Grootel, Brandon Chow, Kai Deng, Katherine Lin, Marcos Campos, K. Venkatesh Emani, Vivek Pandit, Victor Shnayder, Wenjing Wang, and Carlo Curino. 2024. NL2SQL is a solved problem... Not!. 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