让隐含前提显性化，用大模型和逻辑推理还原论证链条
Making Implicit Premises Explicit in Logical Understanding of Enthymemes
- 用大模型补全隐含前提，再转为逻辑公式
- 在两个数据集上实现精确的逻辑蕴含判断
- 适合逻辑推理与可解释性研究者使用
现实中的文本和对话通常包含省略前提或结论的论断（即隐含论证）。现有自然语言处理方法能识别隐含论证，但无法解析其逻辑结构；而基于逻辑的方法依赖充足的知识库公式进行归因推导。为此，我们提出一个三阶段流程：首先用大语言模型根据显式前提和结论生成中间隐含前提；其次用另一大模型将自然语言转化为逻辑公式；最后通过基于SAT求解器的神经符号推理器判断逻辑蕴含关系。我们在两个隐含论证数据集上评估该流程，结果显示在正确选择隐含前提方面表现良好，各项指标如精度、召回率、F1分数和准确率均达到理想水平。
原文摘要 · Abstract (English)
Real-world arguments in text and dialogues are normally enthymemes (i.e. some of their premises and/or claims are implicit). Natural language processing (NLP) methods for handling enthymemes can potentially identify enthymemes in text but they do not decode their underlying logic, whereas logic-based approaches for handling them assume a knowledgebase with sufficient formulae that can be used to decode them via abduction. There is therefore a lack of a systematic method for translating textual components of an enthymeme into a logical argument and generating the logical formulae required for their decoding, and thereby showing logical entailment. To address this, we propose a pipeline that integrates: (1) a large language model (LLM) to generate intermediate implicit premises based on the explicit premise and claim; (2) another LLM to translate the natural language into logical formulas; and (3) a neuro-symbolic reasoner based on a SAT solver to determine entailment. We evaluate our pipeline on two enthymeme datasets, demonstrating promising performance in selecting the correct implicit premise, as measured by precision, recall, F1-score, and accuracy.
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