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.<\/p>\n
<\/p>\n
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.<\/p>\n","protected":false},"excerpt":{"rendered":"
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 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