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

Abstract

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).