TabSQLify: Enhancing Reasoning Capabilities of LLMs Through Table Decomposition

arXiv:2404.10150v1 Announce Type: new
Abstract: Table reasoning is a challenging task that requires understanding both natural language questions and structured tabular data. Large language models (LLMs) have shown impressive capabilities in natural language understanding and generation, but they often struggle with large tables due to their limited input length. In this paper, we propose TabSQLify, a novel method that leverages text-to-SQL generation to decompose tables into smaller and relevant sub-tables, containing only essential information for answering questions or verifying statements, …

abstract arxiv capabilities cs.cl cs.db cs.ir data language language models language understanding large language large language models llms natural natural language questions reasoning struggle table tables tabular tabular data through type understanding