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Cited by 157Topic ModelingMultimodal Machine Learning ApplicationsNatural Language Processing Techniques

The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.

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

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Sui, Yuan, Zhou, Mengyu, Zhou, Mingjie, Han, Shi, Zhang, Dongmei·2024·Proceedings of the 17th ACM International Conference on Web Search and Data Mining
DOI 10.1145/3616855.3635752
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Abstract

Large language models (LLMs) are becoming attractive as few-shot reasoners to solve Natural Language (NL)-related tasks. However, there is still much to learn about how well LLMs understand structured data, such as tables. Although tables can be used as input to LLMs with serialization, there is a lack of comprehensive studies that examine whether LLMs can truly comprehend such data. In this paper, we try to understand this by designing a benchmark to evaluate the structural understanding capabilities (SUC) of LLMs. The benchmark we create includes seven tasks, each with its own unique challenges, \eg, cell lookup, row retrieval, and size detection. We perform a series of evaluations on GPT-3.5 and GPT-4. We find that performance varied depending on several input choices, including table input format, content order, role prompting, and partition marks. Drawing from the insights gained through the benchmark evaluations, we proposeself-augmentation for effective structural prompting, such as critical value / range identification using internal knowledge of LLMs. When combined with carefully chosen input choices, these structural prompting methods lead to promising improvements in LLM performance on a variety of tabular tasks, \eg, TabFact(\uparrow2.31%), HybridQA(\uparrow2.13%), SQA(\uparrow2.72%), Feverous(\uparrow0.84%), and ToTTo(\uparrow5.68%). We believe that our open-source (please find code and data at https://github.com/microsoft/TableProvider) benchmark and proposed prompting methods can serve as a simple yet generic selection for future research.

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Research works printed but not linked: 16

  1. 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 · arXiv
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  5. Longxu Dou Yan Gao Mingyang Pan Dingzirui Wang Wanxiang Che Dechen Zhan and Jian-Guang Lou. 2022. UniSAr: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQL. arXiv:2203.07781 [cs] http: //arxiv.org/abs/2203.07781 · arXiv
  6. Xinyi He Mengyu Zhou Jialiang Xu Xiao Lv Tianle Li Yijia Shao Shi Han Zejian Yuan and Dongmei Zhang. 2022. Inferring Tabular Analysis Metadata by Infusing Distribution and Knowledge Information. https://doi.org/10.48550/ arXiv.2209.00946 arXiv:2209.00946 [cs] · arXiv
  7. 10.18653/v1/2020.acl-main.398 · DOI
  8. Madelon Hulsebos and Paul Groth. 2022. GitTables: A LargeScale Corpus of Relational Tables. https://doi.org/10.48550/arXiv.2106.07258 arXiv:2106.07258 [cs] · DOI
  9. 10.48550/arXiv.2105 · DOI
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  11. Stephanie Lin Jacob Hilton and Owain Evans. 2022. TruthfulQA: Measuring How Models Mimic Human Falsehoods. arXiv:2109.07958 [cs] http://arxiv.org/ abs/2109.07958 · arXiv
  12. OpenAI. 2023. GPT-4 Technical Report. https://doi.org/10.48550/arXiv.2303.08774 arXiv:2303.08774 [cs] · DOI · Paper page
  13. Ankur P. Parikh Xuezhi Wang Sebastian Gehrmann Manaal Faruqui Bhuwan Dhingra Diyi Yang and Dipanjan Das. 2020. ToTTo: A Controlled TableTo-Text Generation Dataset. https://doi.org/10.48550/arXiv.2004.14373 arXiv:2004.14373 [cs] · DOI · Paper page
  14. Alon Talmor Ori Yoran Amnon Catav Dan Lahav Yizhong Wang Akari Asai Gabriel Ilharco Hannaneh Hajishirzi and Jonathan Berant. 2021. MultiModalQA: Complex Question Answering over Text Tables and Images. https://doi.org/10. 48550/arXiv.2104.06039 arXiv:2104.06039 [cs] · arXiv
  15. 10.48550/arXiv.2009.06732 · DOI · Paper page
  16. 10.48550/arXiv.2203 · DOI

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Metadata

Authors
Sui, Y., Zhou, M., Zhou, M., Han, S., & Zhang, D.
Affiliations
  • National University of Singapore (SG)
  • Microsoft Research Asia (China) (CN)
ORCID
Venue
Proceedings of the 17th ACM International Conference on Web Search and Data Mining
Publisher
ACM
Published
2024-03-04
Pages
645-654
DOI
10.1145/3616855.3635752
OpenAlex
W4392366650
Cited by
155
References
33
Citations by year
2026: 59 · 2025: 72 · 2024: 24
FWCI
45.74
Type
conference paper
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • ACM International Conference on Web Search and Data Mining (WSDM) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
Keywords
large language models, self-augmentation, GPT-3.5, GPT-4

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