Summary of 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, By Jingde Cheng
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
by Jingde Cheng
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research proposes a novel solution to overcome the limitations of pre-training large language models (LLMs): Automated generation of massive reasonable empirical theorems by forward reasoning based on strong relevant logics. The approach is part of a broader effort to tackle challenges in Automated Theorem Finding (ATF) and Automated Knowledge Appreciation (AKA). By leveraging advanced logic-based methods, this paper aims to revitalize the pre-training process for LLMs, enabling further advancements in natural language processing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research tries to fix a big problem with training huge language models. Right now, we don’t have enough data to train these models properly. The solution is to create many important mathematical statements using logical reasoning. This helps us find new theorems and understand knowledge better. By doing this, we can make language processing more powerful. |
Keywords
» Artificial intelligence » Natural language processing
