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Attribution Gradients: Incrementally Unfolding Citations for Critical Examination of Attributed AI Answers
- 将支持/反驳语句、来源链接、上下文解释整合到一处
- 可直接展开二级引用，追溯信息源头
- 实验证明能提升读者对文献的深度理解
AI问答引擎不再返回文档列表，而是生成带内联引用的答案。但阅读引用来源成本高，且链接无法提示内容。本文提出归属梯度（Attribution Gradients），通过整合证据量、支持或反驳的摘录、来源链接及上下文解释于一体，并支持在原位置展开二级引用。实验室研究显示，该方法显著提升了用户在批判性阅读任务中的参与深度，使读者从来源中获取的信息比标准引用和文档问答设计更多。
原文摘要 · Abstract (English)
AI answer engines are a relatively new kind of information search tool: rather than returning a ranked list of documents, they generate an answer to a search question with inline citations to sources. But reading the cited sources is costly, and citation links themselves offer little guidance about what evidence they contain. We present attribution gradients, a technique to boost the informativeness of citations by consolidating scent and information prey in place. Its first feature is bringing evidence amounts, supporting/contradictory excerpts, links to source, contextual explanation into one place. Its second feature is the ability to unravel second-degree citations in place. In a lab study we demonstrate usage of the full gradient in a critical reading task and its support for deep engagement that increased the depth of what readers took away from the sources versus a standard citation and document QA design.
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