Table-GPT: Table Fine-tuned GPT for Diverse Table Tasks
Authors: 
Peng Li, Yeye He, Dror Yashar, Weiwei Cui, Song Ge, Haidong Zhang, Danielle Rifinski Fainman, Dongmei Zhang, and Surajit ChaudhuriAuthors Info & Claims
Peng Li, Yeye He, Dror Yashar, Weiwei Cui, Song Ge, Haidong Zhang, Danielle Rifinski Fainman, Dongmei Zhang, and Surajit ChaudhuriAuthors Info & ClaimsArticle No.: 176, Pages 1 - 28
Abstract
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.
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Published in PACMMOD Volume 2, Issue 3
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