Long-form question answering (LFQA) poses a challenge as it involves
generating detailed answers in the form of paragraphs, which go beyond simple
yes/no responses or short factual answers. While existing QA models excel in
questions with concise answers, LFQA requires handling multiple topics and
their intricate relationships, demanding comprehensive explanations. Previous
attempts at LFQA focused on generating long-form answers by utilizing relevant
contexts from a corpus, relying solely on the question itself. However, they
overlooked the possibility that the question alone might not provide sufficient
information to identify the relevant contexts. Additionally, generating
detailed long-form answers often entails aggregating knowledge from diverse
sources. To address these limitations, we propose an LFQA model with iterative
Planning, Retrieval, and Generation. This iterative process continues until a
complete answer is generated for the given question. From an extensive
experiment on both an open domain and a technical domain QA dataset, we find
that our model outperforms the state-of-the-art models on various textual and
factual metrics for the LFQA task.

提出了一种具有迭代规划、检索和生成过程的长篇问答（Long-form question answering，LFQA）模型，该模型通过迭代过程直到生成给定问题的完整答案，解决了既有模型在处理复杂问题和知识聚合方面的限制，并在各种文本和实际指标上优于现有模型。