MATATA: a weak-supervised MAthematical Tool-Assisted reasoning for
  Tabular Applications
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.
674e0cc59dd6b1da2c9359d0
Vishnou Vinayagame
claimed_verified
2024-12-02T22:07:16.212Z
644c90ae8c51ddbe0eaa22bd
https://cdn-avatars.huggingface.co/v1/production/uploads/644c90ae8c51ddbe0eaa22bd/cy3rJZBj_LcItsLTcJvrk.jpeg
Vishnou Vinayagame
user
Vishnou
674e0cc59dd6b1da2c9359d1
Gregory Senay
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2024-12-02T22:07:14.183Z
63ee763cf599efc7a0147220
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Gregory Senay
user
GregorySenay
674e0cc59dd6b1da2c9359d2
Luis Martí
MATATA: a weak-supervised MAthematical Tool-Assisted reasoning for
  Tabular Applications
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.
MATATA trains open-source, cost-effective LLM agents for tabular data via reasoning and iterative weak supervision, achieving state-of-the-art performance on FinQA and TAT-QA, and competitive results on TabMWP.
LLM agents tabular data reasoning planning tool use progressive self-improvement iterative weak supervision small language models (SLMs) local hosting data privacy flexible tools scalability FinQA TAT-QA TabMWP GPT-4 based frameworks