Making Implicit Premises Explicit in Logical Understanding of Enthymemes
Researchers have proposed a method to translate real-world arguments into logical formulas. Their pipeline uses two large language models to identify implicit premises in text and convert them into logical formulas, which are then used by a neuro-symbolic reasoner to determine the correctness of the argument. The authors tested their approach on two datasets and found it to be effective in selecting the correct implicit premise.
Researchers have proposed a method to translate real-world arguments into logical formulas. Their pipeline uses two large language models to identify implicit premises in text and convert them into logical formulas, which are then used by a neuro-symbolic reasoner to determine the correctness of the argument. The authors tested their approach on two datasets and found it to be effective in selecting the correct implicit premise.
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Why it matters: This work matters because it addresses a long-standing challenge in natural language processing: understanding real-world arguments that often rely on unstated assumptions. By developing a systematic method for translating text into logical formulas, this research has implications for improving AI's ability to reason and make decisions based on complex human arguments.
Source: https://arxiv.org/abs/2603.06114
This article was originally published at: https://arxiv.org/abs/2603.06114