Exploring Hint Generation Approaches in Open-Domain Question Answering
This work addresses the challenge of improving answer accuracy in QA systems, offering a potentially incremental advancement over existing methods.
The paper tackled the problem of context preparation in open-domain question answering by introducing HINTQA, a novel approach that uses automatic hint generation instead of traditional retrieval or generation methods, and demonstrated that it surpasses these approaches on three QA datasets.
Automatic Question Answering (QA) systems rely on contextual information to provide accurate answers. Commonly, contexts are prepared through either retrieval-based or generation-based methods. The former involves retrieving relevant documents from a corpus like Wikipedia, whereas the latter uses generative models such as Large Language Models (LLMs) to generate the context. In this paper, we introduce a novel context preparation approach called HINTQA, which employs Automatic Hint Generation (HG) techniques. Unlike traditional methods, HINTQA prompts LLMs to produce hints about potential answers for the question rather than generating relevant context. We evaluate our approach across three QA datasets including TriviaQA, NaturalQuestions, and Web Questions, examining how the number and order of hints impact performance. Our findings show that the HINTQA surpasses both retrieval-based and generation-based approaches. We demonstrate that hints enhance the accuracy of answers more than retrieved and generated contexts.