Case-Based Deduction for Entailment Tree Generation

Jihao Shi, Xiao Ding () and Ting Liu
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Jihao Shi: School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
Xiao Ding: School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
Ting Liu: School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China

Mathematics, 2024, vol. 12, issue 18, 1-20

Abstract: Maintaining logical consistency in structured explanations is critical for understanding and troubleshooting the reasoning behind a system’s decisions. However, existing methods for entailment tree generation often struggle with logical consistency, resulting in erroneous intermediate conclusions and reducing the overall accuracy of the explanations. To address this issue, we propose case-based deduction ( CBD ), a novel approach that retrieves cases with similar logical structures from a case base and uses them as demonstrations for logical deduction. This method guides the model toward logically sound conclusions without the need for manually constructing logical rule bases. By leveraging a prototypical network for case retrieval and reranking them using information entropy, CBD introduces diversity to improve in-context learning. Our experimental results on the EntailmentBank dataset show that CBD significantly improves entailment tree generation, achieving performance improvements of 1.7% in Task 1, 0.6% in Task 2, and 0.8% in Task 3 under the strictest Overall AllCorrect metric. These findings confirm that CBD enhances the logical consistency and overall accuracy of AI systems in structured explanation tasks.

Keywords: explainable AI; entailment tree generation; case-based reasoning; prototypical network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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