# Towards Zero-Shot and Few-Shot Table Question Answering using GPT-3

Source: [Towards Zero-Shot and Few-Shot Table Question Answering using GPT-3](<https://www.sota2.com/research/paper/68fa6dc31e209131cea3bf29>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:43:12.182Z

Authors: Pragya Srivastava, Tanuja Ganu, Saikat Guha

Published: 2022-10-31T13:08:55.000Z

## Abstract

We present very early results on using GPT-3 to perform question answering on tabular data. We find that stock pre-trained GPT-3 is able to zero-shot learn the table structure from a serialized JSON array-of-arrays representation, and able to answer lookup queries and simple comparison questions in natural language without any fine-tuning. We further find that simple prompt engineering to include few-shot static Q&A examples significantly improves accuracy. Lastly, we find that intermixing passage text improves accuracy even further on heterogeneous data. We apply our approach on a novel dataset of simple tables in newspaper infographics with promising results. Overall, we find much cause for optimism in this basic approach.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2210.17284>)

[PDF](<https://arxiv.org/pdf/2210.17284.pdf>)