用强相关逻辑自动生成海量合理定理，缓解大模型训练数据枯竭问题。

Automated Generation of Massive Reasonable Empirical Theorems by Forward Reasoning Based on Strong Relevant Logics -- A Solution to the Problem of LLM Pre-training Data Exhaustion

• 基于强相关逻辑进行正向推理生成定理
• 可批量产出大量合理且可用的数学经验命题
• 适合大模型训练数据增强与自动知识发现场景

近期常有观点认为大语言模型（LLMs）预训练数据已耗尽。本文提出一种解决方案：基于强相关逻辑的正向推理，自动化生成海量合理的经验定理。这实质上是解决自动定理发现（ATF）与自动知识欣赏（AKA）问题的一部分方法。通过该机制，可在不依赖人工标注的前提下，持续生成具备逻辑合理性与数学价值的命题，为大模型训练提供新数据源，缓解数据瓶颈。

原文摘要 · Abstract (English)

Recently, it is often said that the data used for the pre-training of large language models (LLMs) have been exhausted. This paper proposes a solution to the problem: Automated generation of massive reasonable empirical theorems by forward reasoning based on strong relevant logics. In fact, this can be regarded as a part of our approach to the problems of ATF (Automated Theorem Finding) and AKA (Automated Knowledge Appreciation).

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