Abstract
Question-answering datasets require a broad set of reasoning skills. We show how to use question decompositions to teach language models these broad reasoning skills in a robust fashion. Specifically, we use widely available QDMR representations to programmatically create synthetic contexts for real questions in six multihop reasoning datasets. These contexts are carefully designed to avoid common reasoning shortcuts prevalent in real contexts that prevent models from learning the right skills. This results in a pretraining dataset, named TeaBReaC, containing 525K multihop questions (with associated formal programs) covering about 900 reasoning patterns. We show that pretraining standard language models (LMs) on TeaBReaC before fine-tuning them on target datasets improves their performance by up to 13 EM points across 3 multihop QA datasets, with a 30 point gain on more complex questions. The resulting models also demonstrate higher robustness, with a 6-11 point improvement on two contrast sets. Furthermore, TeaBReaC pretraining substantially improves model performance and robustness even when starting with numeracy-aware LMs pretrained using recent methods (e.g., PReasM). Our work thus shows how one can effectively use decomposition-guided contexts to robustly teach multihop reasoning.
Abstract (translated)
URL
https://arxiv.org/abs/2205.12496
