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.

以IRAC框架为灵感，我们引入了一种新的提示方法-逻辑链，通过分解（作为独立的逻辑线索解决元素）和重组（将这些子答案重新组合以解决潜在的逻辑表达式）来引发基于规则的推理。我们在涉及三个不同组合规则的八个基于规则的推理任务中对逻辑链进行了评估，并使用开源和商业化的语言模型展示其一贯优于其他提示方法，包括思维链和自问。

逻辑链：基于大语言模型的基于规则的推理