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Long-form question answering (LFQA) poses a challenge as it involvesgenerating detailed answers in the form of paragraphs, which go beyond simpleyes/no responses or short factual answers. While existing QA models excel inquestions with concise answers, LFQA requires handling multiple topics andtheir intricate relationships, demanding comprehensive explanations. Previousattempts at LFQA focused on generating long-form answers by utilizing relevantcontexts from a corpus, relying solely on the question itself. However, theyoverlooked the possibility that the question alone might not provide sufficientinformation to identify the relevant contexts. Additionally, generatingdetailed long-form answers often entails aggregating knowledge from diversesources. To address these limitations, we propose an LFQA model with iterativePlanning, Retrieval, and Generation. This iterative process continues until acomplete answer is generated for the given question. From an extensiveexperiment on both an open domain and a technical domain QA dataset, we findthat our model outperforms the state-of-the-art models on various textual andfactual metrics for the LFQA task.

Long-form Question Answering: An Iterative Planning-Retrieval-Generation Approach