Conditional question answering (CQA) is an important task that aims to find probable answers and identify conditions that need to be satisfied to support the answer. Existing approaches struggle with CQA due to two main challenges: (1) precisely identifying conditions and their logical relationship, and (2) verifying and solving the conditions. To address these challenges, we propose Chain of Condition, a novel prompting approach by firstly identifying all conditions and constructing their logical relationships explicitly according to the document, then verifying whether these conditions are satisfied, finally solving the logical expression by tools to indicate any missing conditions and generating the answer based on the resolved conditions. The experiments on two benchmark conditional question answering datasets shows chain of condition outperforms existing prompting baselines, establishing a new state-of-the-art. Furthermore, with backbone models like GPT-3.5-Turbo or GPT-4, it surpasses all supervised baselines with only few-shot settings.
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