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We train a language model (LM) to robustly answer multistep questions bygenerating and answering sub-questions. We propose Chain-of-Questions, aframework that trains a model to generate sub-questions and sub-answers one ata time by leveraging human annotated question decomposition meaningrepresentation (QDMR). The key technical challenge is that QDMR only containssub-questions but not answers to those sub-questions, so we treat sub-answersas latent variables and optimize them using a novel dynamic mixture of Hard-EMand MAPO. Chain-of-Questions greatly outperforms strong neuro-symbolic methodsby 9.0 F1 on DROP contrast set, and outperforms GPT-3.5 by 24.3 F1 on HOTPOTQAadversarial set, thus demonstrating the effectiveness and robustness of ourframework.

Chain-of-Questions Training with Latent Answers for Robust Multistep Question Answering