Query Refinement Prompts for Closed-Book Long-Form Question Answering
This work addresses the problem of evaluating long-form outputs in AI for researchers and practitioners, though it is incremental as it builds on existing LLM capabilities.
The paper tackles the challenge of evaluating long-form question answering by proposing query refinement prompts that encourage LLMs to address multifaceted questions, achieving performance that outperforms fully finetuned models in closed-book settings and matches retrieve-then-generate open-book models on ASQA and AQuAMuSe datasets.
Large language models (LLMs) have been shown to perform well in answering questions and in producing long-form texts, both in few-shot closed-book settings. While the former can be validated using well-known evaluation metrics, the latter is difficult to evaluate. We resolve the difficulties to evaluate long-form output by doing both tasks at once -- to do question answering that requires long-form answers. Such questions tend to be multifaceted, i.e., they may have ambiguities and/or require information from multiple sources. To this end, we define query refinement prompts that encourage LLMs to explicitly express the multifacetedness in questions and generate long-form answers covering multiple facets of the question. Our experiments on two long-form question answering datasets, ASQA and AQuAMuSe, show that using our prompts allows us to outperform fully finetuned models in the closed book setting, as well as achieve results comparable to retrieve-then-generate open-book models.