Large Language Models are few(1)-shot Table Reasoners

By Wenhu Chen. 2022

236 citations recorded by the source corpus.

Recent literature has shown that large language models (LLMs) are generally excellent few-shot reasoners to solve text reasoning tasks. However, the capability of LLMs on table reasoning tasks is yet to be explored. In this paper, we aim at understanding how well LLMs can perform table-related tasks with few-shot in-context learning. Specifically, we evaluated LLMs on popular table QA and fact verification datasets like WikiTableQuestion, FetaQA, TabFact, and FEVEROUS and found that LLMs are competent at complex reasoning over table structures, though these models are not pre-trained on any table corpus. When combined with ‘chain of thoughts’ prompting, LLMs can achieve very strong performance with only a 1-shot demonstration, even on par with some SoTA models. We show that LLMs are even more competent at generating comprehensive long-form answers on FetaQA than tuned T5-large. We further manually studied the reasoning chains elicited from LLMs and found that these reasoning chains are highly consistent with the underlying semantic form. We believe that LLMs can serve as a simple yet generic baseline for future research. The code and data are released in {url{https://github.com/wenhuchen/TableCoT}.

Subfields: Artificial Intelligence

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Citations in the corpus

Towards Reasoning in Large Language Models: A Survey (2022, background): “Chen (2022) apply CoT to table-based reasoning, finding that LLMs can achieve strong performance on table tasks with only one exemplar.”
StructGPT: A General Framework for Large Language Model to Reason over Structured Data (2023, methodology): “The first type of method linearizes the structured data into a sentence ( e.g., table rows), and feeds it into the LLMs to generate the answer according to in-context exemplars (Cheng et al., 2022; Chen, 2023).”
DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction (2023, background): “For instance, the database schema for question “What are the average and maximum capacities for all stadiums?” included a column named “aver-age”, which was selected by the model instead of taking the average of the capacity column.”
Reasoning with Language Model Prompting: A Survey (2022, unknown): “, 2022b), Table (Chen, 2022), AlgoPrompt (Zhou et al.”