标题： 归属梯度：逐步展开引用以批判性审查归属AI答案 显示英文标题

标题： Attribution Gradients: Incrementally Unfolding Citations for Critical Examination of Attributed AI Answers

摘要： 人工智能问答系统越来越多地生成带有来源引用的响应。 然而，在大多数情况下，验证这些引用的实际内容是不切实际的。 在本文中，我们提出了引用梯度作为解决方案。 引用梯度提供了集成的、逐步的便利性，以便深入查看一个引用段落。 用户可以将答案中的一个句子分解为其主张。 对于每个主张，用户可以查看从来源中挖掘的支持性和矛盾性的摘录。 这些摘录作为可点击的通道进入来源（在我们的应用中，是科学论文）。 当证据本身包含更多引用时，用户界面会将证据拆解为被引用来源的摘录。 这些引用梯度的特性促进了答案、主张、摘录和上下文之间的并行相互连接。 在可用性研究中，我们观察到用户对来源的参与度更高，并且在参与者使用引用梯度和基线修订带有引用的AI回答的任务中，修订内容更加丰富。 显示英文摘要

摘要： AI question answering systems increasingly generate responses with attributions to sources. However, the task of verifying the actual content of these attributions is in most cases impractical. In this paper, we present attribution gradients as a solution. Attribution gradients provide integrated, incremental affordances for diving into an attributed passage. A user can decompose a sentence of an answer into its claims. For each claim, the user can view supporting and contradictory excerpts mined from sources. Those excerpts serve as clickable conduits into the source (in our application, scientific papers). When evidence itself contains more citations, the UI unpacks the evidence into excerpts from the cited sources. These features of attribution gradients facilitate concurrent interconnections among answer, claim, excerpt, and context. In a usability study, we observed greater engagement with sources and richer revision in a task where participants revised an attributed AI answer with attribution gradients and a baseline.