MATATA: a weak-supervised MAthematical Tool-Assisted reasoning for
  Tabular Applications
Authors: name: Vishnou Vinayagame, name: Gregory Senay, name: Luis Martí

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.