MATATA: A weakly-supervised MAthematical Tool-Assisted reasoning for Tabular Applications

• URL: http://arxiv.org/abs/2411.18915v3
• Date: Tue, 10 Dec 2024 19:18:10 GMT
• Title: MATATA: A weakly-supervised MAthematical Tool-Assisted reasoning for Tabular Applications
• Authors: Vishnou Vinayagame, Gregory Senay, Luis Martí,
• Abstract summary: MATATA is a cost-effective method to train LLM agents for data problems through reasoning, planning, and tool use.<n>It empowers 3.8B/8B Small Language Models (SLMs), particularly suited for local hosting and sensitive business contexts.<n>Experiments show that MATATA reaches state-of-the-art performances on FinQA and TAT-QA among reasoning frameworks based on open-source models.
• Score: 0.9831489366502302
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Mathematical reasoning capabilities are increasing with tool-augmented language agents, but methods often rely either on closed-source or large models, external data, or extensive prompt engineering. This work introduces MATATA, a novel cost-effective method to train LLM agents for tabular data problems through reasoning, planning, and tool use. With a progressive self-improvement paradigm and an iterative weak supervision, it empowers 3.8B/8B Small Language Models (SLMs), particularly suited for local hosting and sensitive business contexts where data privacy is crucial. By employing a flexible and reusable tools across different datasets, it achieves robust performance with effective scalability across shared tasks. Experiments show that MATATA reaches state-of-the-art performances on FinQA and TAT-QA among reasoning frameworks based on open-source models. Moreover, MATATA models compete with GPT-4 based frameworks on TabMWP, while being SLMs.

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