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        <title>Chain of Logic: Rule-Based Reasoning with Large Language Models</title>
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    <abstract>Rule-based reasoning, a fundamental type of legal reasoning, enables us to draw conclusions by accurately applying a rule to a set of facts. We explore causal language models as rule-based reasoners, specifically with respect to compositional rules - rules consisting of multiple elements which form a complex logical expression. Reasoning about compositional rules is challenging because it requires multiple reasoning steps, and attending to the logical relationships between elements. We introduce a new prompting method, Chain of Logic, which elicits rule-based reasoning through decomposition (solving elements as independent threads of logic), and recomposition (recombining these sub-answers to resolve the underlying logical expression). This method was inspired by the IRAC (Issue, Rule, Application, Conclusion) framework, a sequential reasoning approach used by lawyers. We evaluate chain of logic across eight rule-based reasoning tasks involving three distinct compositional rules from the LegalBench benchmark and demonstrate it consistently outperforms other prompting methods, including chain of thought and self-ask, using open-source and commercial language models.</abstract>
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%0 Conference Proceedings
%T Chain of Logic: Rule-Based Reasoning with Large Language Models
%A Servantez, Sergio
%A Barrow, Joe
%A Hammond, Kristian
%A Jain, Rajiv
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F servantez-etal-2024-chain
%X Rule-based reasoning, a fundamental type of legal reasoning, enables us to draw conclusions by accurately applying a rule to a set of facts. We explore causal language models as rule-based reasoners, specifically with respect to compositional rules - rules consisting of multiple elements which form a complex logical expression. Reasoning about compositional rules is challenging because it requires multiple reasoning steps, and attending to the logical relationships between elements. We introduce a new prompting method, Chain of Logic, which elicits rule-based reasoning through decomposition (solving elements as independent threads of logic), and recomposition (recombining these sub-answers to resolve the underlying logical expression). This method was inspired by the IRAC (Issue, Rule, Application, Conclusion) framework, a sequential reasoning approach used by lawyers. We evaluate chain of logic across eight rule-based reasoning tasks involving three distinct compositional rules from the LegalBench benchmark and demonstrate it consistently outperforms other prompting methods, including chain of thought and self-ask, using open-source and commercial language models.
%U https://aclanthology.org/2024.findings-acl.159
%P 2721-2733

Markdown (Informal)

[Chain of Logic: Rule-Based Reasoning with Large Language Models](https://aclanthology.org/2024.findings-acl.159) (Servantez et al., Findings 2024)

• Chain of Logic: Rule-Based Reasoning with Large Language Models (Servantez et al., Findings 2024)

ACL

• Sergio Servantez, Joe Barrow, Kristian Hammond, and Rajiv Jain. 2024. Chain of Logic: Rule-Based Reasoning with Large Language Models. In Findings of the Association for Computational Linguistics ACL 2024, pages 2721–2733, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.