Chain of Logic: Rule-Based Reasoning with Large Language Models
Sergio Servantez, Joe Barrow, Kristian Hammond, Rajiv Kumar Jain · 2024
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 rulebased 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.