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Reasoning Factual Knowledge in Structured Data with Large Language Models by Huang, Sirui; Gu, Yanggan; Hu, Xuming; Li, Zhonghao; Li, Qing; Xu, Guandong is a scholarly article available to read on EtoBox.

What is Reasoning Factual Knowledge in Structured Data with Large Language Models about?

Large language models (LLMs) have made remarkable progress in various natural language processing tasks as a benefit of their capability to comprehend and reason with factual knowledge. However, a significant amount of factual knowledge is stored in structured data, which possesses unique characteristics that differ from the unstructured texts used for pretraining. This difference can introduce imperceptible inference parameter deviations, posing challenges for LLMs in effectively utilizing and reasoning with structured data to accurately infer factual knowledge. To this end, we propose a benchmark named StructFact, to evaluate the structural reasoning capabilities of LLMs in inferring factual knowledge. StructFact comprises 8,340 factual questions encompassing various tasks, domains, timelines, and regions. This benchmark allows us to investigate the capability of LLMs across five factual tasks derived from the unique characteristics of structural facts. Extensive experiments on a set of LLMs with different training strategies reveal the limitations of current LLMs in inferring factual knowledge from structured data. We present this benchmark as a compass to navigate the strengths

Author

Huang, Sirui; Gu, Yanggan; Hu, Xuming; Li, Zhonghao; Li, Qing; Xu, Guandong

Published

2024

Language

EN