InFact：用于提升大语言模型事实性的信息量对齐
InFact: Informativeness Alignment for Improved LLM Factuality
AI总结：
针对大语言模型生成事实正确但信息量不足文本的问题，提出信息量对齐机制，利用事实性基准优化答案的正确性与信息量，同时提升模型事实性。
摘要：
事实完整性是一个通用术语，它描述了一段事实正确的文本在细节和信息量上的丰富程度。例如，事实句“巴拉克·奥巴马出生在美国”在事实上是正确的，但不如事实句“巴拉克·奥巴马出生在美国夏威夷州檀香山”信息量大。尽管已知大语言模型倾向于产生幻觉并生成事实上不正确的文本，但它们也可能倾向于选择生成事实上正确但信息量不如其他更具信息量的选择的文本。在这项工作中，我们通过提出一种信息量对齐机制来解决这个问题。该机制利用近期的事实性基准，提出一个信息量对齐目标。该目标优先考虑既正确又具有信息量的答案。我们工作的一个关键发现是，当训练模型以最大化该目标或优化其偏好时，我们不仅能提升信息量，还能提升事实性。
英文摘要：
Factual completeness is a general term that captures how detailed and informative a factually correct text is. For instance, the factual sentence ``Barack Obama was born in the United States'' is factually correct, though less informative than the factual sentence ``Barack Obama was born in Honolulu, Hawaii, United States''. Despite the known fact that LLMs tend to hallucinate and generate factually incorrect text, they might also tend to choose to generate factual text that is indeed factually correct and yet less informative than other, more informative choices. In this work, we tackle this problem by proposing an informativeness alignment mechanism. This mechanism takes advantage of recent factual benchmarks to propose an informativeness alignment objective. This objective prioritizes answers that are both correct and informative. A key finding of our work is that when training a model to maximize this objective or optimize its preference, we can improve not just informativeness but also factuality.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
