用大模型为逻辑规则中的未命名谓词起语义合理的名字。
Predicate Renaming via Large Language Models
- 利用大模型理解自然语言和代码，为无名谓词生成有意义的命名。
- 在人工构造的逻辑规则上验证，大模型能有效提供合理命名建议。
- 适合需要提升逻辑理论可读性和可复用性的研究者使用。
本文研究如何利用大语言模型（LLMs）为逻辑规则中的未命名谓词命名。在归纳逻辑编程中，多种规则生成方法会产生包含未命名谓词的规则，谓词发明是其中关键例子。这严重影响了逻辑理论的可读性、可解释性和可复用性。借助大模型在自然语言和代码理解方面的最新进展，我们探索其为未命名谓词提供语义合理命名建议的能力。在一些人工构造的逻辑规则上的评估表明，大模型在此任务上具有潜力。
原文摘要 · Abstract (English)
In this paper, we address the problem of giving names to predicates in logic rules using Large Language Models (LLMs). In the context of Inductive Logic Programming, various rule generation methods produce rules containing unnamed predicates, with Predicate Invention being a key example. This hinders the readability, interpretability, and reusability of the logic theory. Leveraging recent advancements in LLMs development, we explore their ability to process natural language and code to provide semantically meaningful suggestions for giving a name to unnamed predicates. The evaluation of our approach on some hand-crafted logic rules indicates that LLMs hold potential for this task.
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