用大模型生成隐含前提，补全论证链条中的逻辑缺口。
Identifying Implicit Premises for Logical Reconstruction of Argument Graphs
- 结合大模型生成与符号逻辑推理，自动补全论证中的隐含前提。
- 在微文本论据语料库上验证，能有效识别语义蕴含、矛盾关系。
- 适合需要自动化构建论证图的自然语言推理研究者使用。
从自然语言文本中重构论证图面临挑战，主要源于隐含前提（即省略前提的论证，又称三段论）普遍存在。现有方法中，有基于自然语言处理的三段论识别技术，也有基于归因推理的符号化方法用于补全逻辑表示中的缺失前提。然而，尚缺乏能够生成隐含前提以逻辑证明一对陈述之间已知蕴含或矛盾关系的方法。为此，我们提出一种神经符号流水线：利用大语言模型（LLMs）生成中间隐含前提，并将其转化为逻辑公式，再与显式前提及显式结论的逻辑表达式结合，推导它们之间的逻辑关系（蕴含、矛盾或中立）。该方法在微文本论据语料库（Microtext Argumentative Corpus）上进行了评估。
原文摘要 · Abstract (English)
The logical reconstruction of argument graphs from natural language text is challenging because of the prevalence of enthymemes (i.e., arguments with implicit premises). There are natural language processing methods for identifying enthymemes in text, and there are symbolic methods based on abduction for identifying missing premises in a logical representation of enthymemes. However, there is a need for methods to generate implicit premises to logically show a known entailment or contradiction relationship between a pair of statements. To address this, we propose a neuro-symbolic pipeline that uses large language models (LLMs) to generate intermediate implicit premises that are translated into logical formulae and used with logical formulae representing explicit premises and explicit claims to show the logical relationships between them (entailment, contradiction, or neutrality). Our approach is evaluated on the Microtext Argumentative Corpus.
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