{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T18:40:18Z","timestamp":1791916818772,"version":"4.3.4"},"reference-count":88,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2024,5,29]],"date-time":"2024-05-29T00:00:00Z","timestamp":1716940800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. ACM Manag. Data"],"published-print":{"date-parts":[[2024,5,29]]},"abstract":"<jats:p>Language models, such as GPT-3 and ChatGPT, demonstrate remarkable abilities to follow diverse human instructions and perform a wide range of tasks, using instruction fine-tuning. However, when we test language models with a range of basic table-understanding tasks, we observe that today's language models are still sub-optimal in many table-related tasks, likely because they are pre-trained predominantly on one-dimensional natural-language texts, whereas relational tables are two-dimensional objects. In this work, we propose a new \"\\emphtable fine-tuning '' paradigm, where we continue to train\/fine-tune language models like GPT-3.5 and ChatGPT, using diverse table-tasks synthesized from real tables as training data, which is analogous to \"instruction fine-tuning'', but with the goal of enhancing language models' ability to understand tables and perform table tasks. We show that our resulting \\sys models demonstrate: (1) better table-understanding capabilities, by consistently outperforming the vanilla GPT-3.5 and ChatGPT, on a wide range of table tasks (data transformation, data cleaning, data profiling, data imputation, table-QA, etc.), including tasks that are completely holdout and unseen during training, and (2) strong generalizability, in its ability to respond to diverse human instructions to perform new and unseen table-tasks, in a manner similar to GPT-3.5 and ChatGPT. Our code and data have been released at https:\/\/github.com\/microsoft\/Table-GPT for future research.<\/jats:p>","DOI":"10.1145\/3654979","type":"journal-article","created":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T09:44:53Z","timestamp":1717062293000},"page":"1-28","source":"Crossref","is-referenced-by-count":71,"title":["Table-GPT: Table Fine-tuned GPT for Diverse Table Tasks"],"prefix":"10.1145","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-7150-7633","authenticated-orcid":false,"given":"Peng","family":"Li","sequence":"first","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2824-5299","authenticated-orcid":false,"given":"Yeye","family":"He","sequence":"additional","affiliation":[{"name":"Microsoft Research, Redmond, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4893-6310","authenticated-orcid":false,"given":"Dror","family":"Yashar","sequence":"additional","affiliation":[{"name":"Microsoft Corporation, Tel Aviv, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0870-7628","authenticated-orcid":false,"given":"Weiwei","family":"Cui","sequence":"additional","affiliation":[{"name":"Microsoft Research, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2178-3086","authenticated-orcid":false,"given":"Song","family":"Ge","sequence":"additional","affiliation":[{"name":"Microsoft Research, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2732-7305","authenticated-orcid":false,"given":"Haidong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Microsoft Research, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6132-7432","authenticated-orcid":false,"given":"Danielle","family":"Rifinski Fainman","sequence":"additional","affiliation":[{"name":"Microsoft Corporation, Tel Aviv, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9230-2799","authenticated-orcid":false,"given":"Dongmei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Microsoft Research, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8252-5270","authenticated-orcid":false,"given":"Surajit","family":"Chaudhuri","sequence":"additional","affiliation":[{"name":"Microsoft Research, Redmond, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,5,30]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Rohan Anil Andrew M Dai Orhan Firat Melvin Johnson Dmitry Lepikhin Alexandre Passos Siamak Shakeri Emanuel Taropa Paige Bailey Zhifeng Chen et al. 2023. 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