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TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model Reasoning
Feb. 20, 2024, 5:53 a.m. | Yuan Sui, Jiaru Zou, Mengyu Zhou, Xinyi He, Lun Du, Shi Han, Dongmei Zhang
cs.CL updates on arXiv.org arxiv.org
Abstract: Table-based reasoning has shown remarkable progress in combining deep models with discrete reasoning, which requires reasoning over both free-form natural language (NL) questions and semi-structured tabular data. However, previous table reasoning solutions only consider small-sized tables and exhibit limitations in handling larger tables. In addition, most existing methods struggle to reason over complex questions since they lack essential information or they are scattered in different places. To alleviate these challenges, we propose TAP4LLM as a …
abstract arxiv cs.ai cs.cl data form free language language model large language large language model limitations natural natural language progress provider questions reasoning sampling small solutions structured data table tables tabular tabular data type
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