Chain-of-Questions Training with Latent Answers for Robust Multistep Question Answering
Zhu Wang, Jesse Thomason, Robin Jia · 2023
We propose Chain-of-Questions, a framework that trains a model to robustly answer multistep questions by generating and answering sub-questions.We obtain supervision for subquestions from human-annotated question decomposition meaning representation (QDMR), but QDMR does not include annotated answers to sub-questions.To overcome this technical challenge, we treat sub-answers as latent variables and infer them with a novel dynamic mixture of Hard-EM and MAPO.Chain-of-Questions is effective and robust, greatly outperforming strong neuro-symbolic methods by 9.0 F1 on a DROP contrast set and GPT-3.5 by 24.3 F1 on a HOTPOTQA adversarial set.