呼吁数学研究中公开提示词、环境配置并形式化结果以实现可验证性。
Explanations, Prompts, and Formalizations: Arguments for New Norms in LLM-Enabled Mathematical Research
- 要求公开生成结果所用的提示词和软件环境。
- 强调结果需形式化以便机器验证，提升可复现性。
- 提倡作者提供直观解释，帮助理解LLM生成的复杂结论。
随着大型语言模型（LLMs）最近成功解决多个数学猜想，数学界已制定相关发表规范。然而，现有规范未涵盖生成结果所用提示词和精确软件设置的披露，也未要求结果以可机器验证的形式化方式呈现。本文认为这两点至关重要。此外，由于LLM所得结果可能难以理解，作者有责任提供直观的解释，以增强可读性和可信度。
原文摘要 · Abstract (English)
As several mathematical conjectures have recently been settled using large language models (LLMs), the mathematical community has formulated norms and recommendations regarding the publishing of such results. These norms do not cover the disclosure of the prompts and precise software setup used to obtain those results, nor do they require that results be formalized in a manner that allows for machine verification. I argue that both of these are essential. In addition, since LLM-obtained results may be hard to understand, human authors have the responsibility to invent intuitive explanations.
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