Long-form question answering (LFQA) aims to generate a paragraph-length
answer for a given question. While current work on LFQA using large pre-trained
model for generation are effective at producing fluent and somewhat relevant
content, one primary challenge lies in how to generate a faithful answer that
has less hallucinated content. We propose a new end-to-end framework that
jointly models answer generation and machine reading. The key idea is to
augment the generation model with fine-grained, answer-related salient
information which can be viewed as an emphasis on faithful facts.
State-of-the-art results on two LFQA datasets, ELI5 and MS MARCO, demonstrate
the effectiveness of our method, in comparison with strong baselines on
automatic and human evaluation metrics. A detailed analysis further proves the
competency of our methods in generating fluent, relevant, and more faithful
answers.

我们提出了一种新的端到端框架，用于解决长篇问答的生成问题，重点是如何生成更加真实准确的答案。通过增加细粒度的相关信息来强化生成模型，实验结果表明我们的方法比强基线模型在自动和人工评估指标上表现更好，并且能够生成流畅，相关且更加真实准确的答案。