Prompt-based Conservation Learning for Multi-hop Question Answering (2022)

Abstract: Multi-hop question answering (QA) requires reasoning over multiple documents to answer a complex question and provide interpretable supporting evidence. However, providing supporting evidence is not enough to demonstrate that a model has performed the desired reasoning to reach the correct answer. Most existing multi-hop QA methods fail to answer a large fraction of sub-questions, even if their parent questions are answered correctly. In this paper, we propose the Prompt-based Conservation Learning (PCL) framework for multi-hop QA, which acquires new knowledge from multi-hop QA tasks while conserving old knowledge learned on single-hop QA tasks, mitigating forgetting. Specifically, we first train a model on existing single-hop QA tasks, and then freeze this model and expand it by allocating additional sub-networks for the multi-hop QA task. Moreover, to condition pre-trained language models to stimulate the kind of reasoning required for specific multi-hop questions, we learn soft prompts for the novel sub-networks to perform type-specific reasoning. Experimental results on the HotpotQA benchmark show that PCL is competitive for multi-hop QA and retains good performance on the corresponding single-hop sub-questions, demonstrating the efficacy of PCL in mitigating knowledge loss by forgetting.

## introduction multi-hop qa is a challenging task with the goals of reasoning over multiple scattered documents to predict an answer, and providing explanatory supporting evidence @xcite . by fine-tuning pre-trained language models (plms) with taskspecific data, most existing multi-hop qa models have achieved good performance in both goals @xcite . despite the success of fine-tuned plms on the multi-hop qa task, providing supporting evidence is not enough to demonstrate that a multi-hop qa ## conclusions and future work in this paper, we introduce a novel prompt-based conservation learning framework for multi-hop qa -a framework that retains knowledge from previous component tasks -able to answer questions in a principled way that matches human expectations by answering sub-questions and integrating the answers. by developing soft prompts related to reasoning types during training, we also show that we can condition plms to stimulate and apply the reasoning knowledge required for specific multihop questions. experimental results on multiple multi-hop qa datasets demonstrate the improved performance of pcl over previous multi-hop qa models in multi-hop qa. next, we plan to extend pcl on qa problems with arbitrary hop-counts, and to increase generality by extending soft prompts to handle qa with unrestricted numbers of, and implicit, reasoning types, and non-linear reasoning structures.