Large language models (LLMs) have shown impressive capabilities on numerous natural language processing tasks. However, LLMs still struggle with numerical question answering for financial documents that include tabular and textual data. Recent works have showed the effectiveness of critic agents (i.e., self-correction) for this task given oracle labels. Building upon this framework, this paper examines the effectiveness of the traditional critic agent when oracle labels are not available, and show, through experiments, that this critic agent's performance deteriorates in this scenario. With this in mind, we present an improved critic agent, along with the calculator agent which outperforms the previous state-of-the-art approach (program-of-thought) and is safer. Furthermore, we investigate how our agents interact with each other, and how this interaction affects their performance.

翻译：大语言模型（LLMs）在众多自然语言处理任务中展现出卓越的能力。然而，在处理包含表格与文本数据的金融文档数值问答任务时，LLMs仍面临挑战。近期研究表明，在给定真值标签的条件下，批评智能体（即自校正机制）对此类任务具有显著效果。基于此框架，本文探讨了在缺乏真值标签时传统批评智能体的有效性，并通过实验证明该智能体在此场景下性能显著下降。鉴于此，我们提出一种改进的批评智能体，并引入计算器智能体，该组合在性能上超越了先前最先进的方法（思维链编程），且具有更高的安全性。此外，我们深入探究了智能体间的交互机制及其对整体性能的影响。