title: MATATA: a weak-supervised MAthematical Tool-Assisted reasoning for
    Tabular Applications
summary: 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.
paper_authors._id: 674e0cc59dd6b1da2c9359d0
paper_authors.name: Vishnou Vinayagame
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-02T22:07:16.212Z
paper_authors._id: 644c90ae8c51ddbe0eaa22bd
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/644c90ae8c51ddbe0eaa22bd/cy3rJZBj_LcItsLTcJvrk.jpeg
paper_authors.fullname: Vishnou Vinayagame
paper_authors.type: user
paper_authors.user: Vishnou
paper_authors._id: 674e0cc59dd6b1da2c9359d1
paper_authors.name: Gregory Senay
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-02T22:07:14.183Z
paper_authors._id: 63ee763cf599efc7a0147220
paper_authors.avatarUrl: /avatars/2361ffb54ba84c5def8f2c9d8bd1769a.svg
paper_authors.fullname: Gregory Senay
paper_authors.type: user
paper_authors.user: GregorySenay
paper_authors._id: 674e0cc59dd6b1da2c9359d2
paper_authors.name: Luis Martí
paper_title: MATATA: a weak-supervised MAthematical Tool-Assisted reasoning for
    Tabular Applications
paper_summary: 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.
paper_ai_keywords: MATATA LLM agents tabular data problems reasoning planning tool use progressive self-improvement iterative weak supervision Small Language Models (SLMs) data privacy robust performance effective scalability FinQA TAT-QA TabMWP GPT-4 based frameworks