Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study-Reference-Cited by-同舟云学术

Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study

Published:2024-03-04 Issue: Volume: Page:
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Container-title:Proceedings of the 17th ACM International Conference on Web Search and Data Mining
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Author:

Sui Yuan¹^ORCID,Zhou Mengyu²^ORCID,Zhou Mingjie³^ORCID,Han Shi²^ORCID,Zhang Dongmei²^ORCID

Affiliation:

1. National University of Singapore, Singapore, Singapore

2. Microsoft, Beijing, China

3. The University of Hong Kong, Hong Kong, Hong Kong

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3616855.3635752

Reference41 articles.

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2. Armen Aghajanyan, Dmytro Okhonko, Mike Lewis, Mandar Joshi, Hu Xu, Gargi Ghosh, and Luke Zettlemoyer. 2021. HTLM: Hyper-Text Pre-Training and Prompting of Language Models. arXiv:2107.06955 [cs] http://arxiv.org/abs/2107.06955

3. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Dhariwal, et al. 2020. Language Models Are Few-Shot Learners. In Advances in Neural Information Processing Systems, Vol. 33. Curran Associates, Inc., 1877--1901. https://proceedings.neurips.cc/paper/2020/hash/ 1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html

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