TableRAG: A Novel Approach for Augmenting LLMs with Information from Retrieved Tables

• Elvis A. de Souza PUC-Rio https://orcid.org/0000-0001-9373-7412
• Patricia F. da Silva Petrobras https://orcid.org/0009-0004-3415-1904
• Diogo Gomes Petrobras https://orcid.org/0000-0001-9351-7647
• Vitor Batista Petrobras https://orcid.org/0000-0002-9095-0266
• Evelyn Batista PUC-Rio https://orcid.org/0000-0001-6672-8318
• Marco Pacheco PUC-Rio https://orcid.org/0000-0002-2381-0183

DOI: https://doi.org/10.5753/stil.2024.245371

Resumo

We present TableRAG, a novel pipeline designed to integrate tabular data into traditional Retrieval-Augmented Generation (RAG) systems. Our approach is composed of three main parts: (i) generating textual representations of tables; (ii) indexing table representations in vector databases for retrieval, and (iii) employing large language models to generate SQL or Python code for data manipulation over a given table. We assessed the effectiveness of TableRAG by comparing retrieval and re-ranking accuracies over the OTT-QA benchmark and by utilizing both open and closed-source LLMs to generate code for answering questions from the WikiTableQuestions benchmark. Our best results show 86.7% HITS@5 for retrieval and 74% accuracy for Q&A, demonstrating the feasibility of integrating tabular data into RAG systems with high accuracy.

Palavras-chave: retrieval augmented generation, tabular data, information retrieval, generative AI, large language models

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