@inproceedings{li2024table-gpt,
author = {Li, Peng and He, Yeye and Yashar, Dror and Cui, Weiwei and Ge, Song and Zhang, Haidong and Fainman, Danielle Rifinski and Zhang, Dongmei and Chaudhuri, Surajit},
title = {Table-GPT: Table Fine-tuned GPT for Diverse Table Tasks},
booktitle = {SIGMOD 2024},
year = {2024},
month = {June},
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 probing language models using 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 ``table 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 Table-GPT models demonstrate (1) better table-understanding capabilities, by consistently outperforming the vanilla GPT-3.5 and ChatGPT, on a wide range of table tasks, including holdout unseen tasks, and (2) strong generalizability, in its ability to respond to diverse human instructions to perform new table-tasks, in a manner similar to GPT-3.5 and ChatGPT.},
url = {http://approjects.co.za/?big=en-us/research/publication/table-gpt-table-fine-tuned-gpt-for-diverse-table-tasks/},
}