MATATA：一种用于表格应用的弱监督数学工具辅助推理

MATATA: a weak-supervised MAthematical Tool-Assisted reasoning for Tabular Applications

November 28, 2024

作者: Vishnou Vinayagame, Gregory Senay, Luis Martí

cs.AI

摘要

随着工具增强型语言代理的出现，数学推理能力正在增强，但方法往往依赖闭源或大型模型、外部数据或大量提示工程。本研究介绍了MATATA，这是一种新颖且具有成本效益的方法，用于通过推理、规划和工具使用来训练LLM代理解决表格数据问题。采用渐进式自我改进范式和迭代式弱监督，赋予了38亿/80亿小语言模型（SLMs）强大的能力，特别适用于数据隐私至关重要的本地托管和敏感商业环境。通过在不同数据集上采用灵活且可重用的工具，实现了在共享任务中有效扩展性的稳健性能。实验表明，MATATA在基于开源模型的推理框架中在FinQA和TAT-QA上达到了最先进的性能。此外，MATATA模型在TabMWP上与基于GPT-4的框架竞争，同时仍然是SLMs。

English

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.