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

• URL: http://arxiv.org/abs/2412.12408v1
• Date: Mon, 16 Dec 2024 23:18:17 GMT
• Title: 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
• Authors: Jingde Cheng,
• Abstract summary: It is often said that the data used for the pre-training of large language models (LLMs) have been exhausted.<n>This paper proposes a solution to the problem: Automated generation of massive reasonable empirical theorems by forward reasoning.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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).

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