Language models are not accurate in numerical problems. Their architecture does not allow for anything less than a probabilistic next word. This paper introduces ComputeGPT: an approach of creating a chat model able to answer computational problems through running on-demand code. ComputeGPT converts each question to relevant code, runs the code, and returns the computed answer as part of the chat. We combine this approach with a local browser-based Python interpretation and fine-tuned prompts in order to achieve state-of-the-art efficiency on numerical problems and provide a suitable front-end and safe environment for the code to be executed in.

翻译：语言模型在数值问题上精度不足，其架构仅支持基于概率的逐词生成。本文提出ComputeGPT：一种通过按需运行代码来回答计算问题的聊天模型构建方法。该方法将每个问题转化为对应代码，执行代码，并将计算结果作为对话内容返回。我们通过结合本地浏览器端Python解释器与微调提示词，在数值问题上实现了当前最优效率，同时为代码执行提供了合适的前端界面与安全环境。