Making Implicit Premises Explicit in Logical Understanding of Enthymemes
arXiv:2603.06114v1 Announce Type: new Abstract: 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 s
arXiv:2603.06114v1 Announce Type: new Abstract: 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.
Executive Summary
This article proposes a novel pipeline for translating textual enthymemes into logical arguments, addressing the lack of systematic methods for decoding their underlying logic. The pipeline integrates large language models and a neuro-symbolic reasoner to generate intermediate implicit premises, translate natural language into logical formulas, and determine entailment. The approach demonstrates promising performance on two enthymeme datasets, measured by precision, recall, F1-score, and accuracy.
Key Points
- ▸ Proposed pipeline integrates large language models and neuro-symbolic reasoner
- ▸ Addresses the challenge of decoding implicit premises in enthymemes
- ▸ Demonstrates promising performance on enthymeme datasets
Merits
Novel Approach
The proposed pipeline offers a unique solution to the problem of handling enthymemes, combining the strengths of natural language processing and logic-based approaches.
Improved Accuracy
The approach demonstrates improved performance in selecting the correct implicit premise, as measured by precision, recall, F1-score, and accuracy.
Demerits
Dependence on Large Language Models
The pipeline's reliance on large language models may limit its applicability in scenarios where such models are not available or are not effective.
Limited Domain Knowledge
The approach may struggle with enthymemes that require specialized domain knowledge or complex reasoning.
Expert Commentary
The proposed pipeline represents a significant step forward in addressing the challenge of handling enthymemes, which are ubiquitous in real-world arguments. By combining the strengths of natural language processing and logic-based approaches, the authors demonstrate a promising solution that can improve the accuracy and effectiveness of automated reasoning and argumentation systems. However, further research is needed to address the limitations of the approach, including its dependence on large language models and limited domain knowledge.
Recommendations
- ✓ Further evaluation and refinement of the pipeline to address its limitations
- ✓ Exploration of applications in various domains, including legal, academic, and industrial settings