Attribution Gradients: Incrementally Unfolding Citations for Critical Examination of Attributed AI Answers
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910098495897600 |
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| author | Kambhamettu, Hita Hwang, Alyssa Laban, Philippe Head, Andrew |
| author_facet | Kambhamettu, Hita Hwang, Alyssa Laban, Philippe Head, Andrew |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_00361 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Attribution Gradients: Incrementally Unfolding Citations for Critical Examination of Attributed AI Answers Kambhamettu, Hita Hwang, Alyssa Laban, Philippe Head, Andrew Human-Computer Interaction Artificial Intelligence 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. |
| title | Attribution Gradients: Incrementally Unfolding Citations for Critical Examination of Attributed AI Answers |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2510.00361 |