Chain of Logic: Rule-Based Reasoning with Large Language Models
<|reference_start|>Chain of Logic: Rule-Based Reasoning with Large Language Models: 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.<|reference_end|>
@article{servantez2024chain,
    title={Chain of Logic: Rule-Based Reasoning with Large Language Models},
    author={Sergio Servantez, Joe Barrow, Kristian Hammond, Rajiv Jain},
    journal={arXiv preprint arXiv:2402.10400},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.10400},
    primaryClass={cs.CL}
}