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 counts, supporting/contradictory excerpts, links to sources, and contextual explanations 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.
Figures & tables
Figure 1. Attribution gradients. When using an attribution gradient, users begin with a claim in a generated answer (1) and see color-coded clusters of evidence (2): black indicates direct support; red, direct contradiction; gray, references to sources that support the claim; and pink, references to sources that contradict the claim. Users can explore evidence excerpts (3) that might include citations to secondary references (4). Users can view excerpts in the original PDF (6) with explanations of how the evidence relates to the claim (5). This gradated unfolding of context is designed to help users access a more nuanced understanding of support for a claim. A teaser figure for attribution gradients. The figure displays an attributed AI response with numbered steps: starting with a generated claim (1), followed by color-coded evidence indicators (2), citations (3), and deep excerpts (4). An arrow connects these excerpts to a side-by-side view of the original source document (5, 6). The footer contains a six-stage progress bar: view claims, view evidence types, view evidence, unravel evidence, orient to source, and read evidence in source.
Figure 2. Articulation of support added by attribution gradients. Compared to conventional attributed AI answers, attribution gradients change the cost structure of looking up information about evidence by introducing new scent, lesser cost, or partial prey at the steps of interaction marked by a blue box. The theoretical support of standard RAG and attribution gradients. Compares attribution gradients to conventional answers across interaction steps like "viewing 2nd-deg evidence". Attribution gradients are shown to provide high "scent" at "low cost" for these tasks by providing "partial prey" (snippets and highlights) that conventional answers do not support.
Figure 3. Unraveling citations. If a piece of second-degree evidence cites another source for its evidence, users can sometimes unravel the citation to that source. After clicking the button to expand nested citations, excerpts from the cited source are shown that pertain to the claim. In this case, evidence from citation “[2]” is shown that more precisely conveys how one of the cited studies shows that “ideating with generative AI decreases diversity.” A user is unraveling citations in the attribution gradients interface. By clicking an "expand nested citations" button, the user has unraveled a citation (marked as "[2]") to view evidence from a cited source.
Figure 4. The full gradient. A reader can (1) decompose a sentence in the generated answer into claims, and (2) view evidence for a particular claim. A reader can see that attributed evidence is mixed for this claim. If a reader clicks on an evidence excerpt (3), they can (4) view it in the source and (5) read context for how the evidence excerpt relates to the claim. Counts shown in the evidence overview indicate the number of evidence excerpts of each type. The attribution gradients interface showing the decomposition of an AI-generated sentence into individual "inspectable claims". After clicking a magnifying glass icon next to a sentence about ChatGPT's effect on creativity, the interface reveals two atomic claims. Each claim is paired with a segmented, color-coded horizontal bar showing the distribution of evidence:
Figure 5. Comparisons of revised claims according to six quality dimensions. Participants using attribution gradients produced revisions that were rated significantly higher than the baseline on all dimensions except evidence quantity. Each box spans the interquartile range of grades, with the horizontal line marking the median. Each individual point is one participant’s grade. Brackets and asterisks indicate significance from paired Wilcoxon signed-rank tests comparing conditions within each dimension (** p<.01 ). These box and whisker plots compare the quality of revised claims made by participants using attribution gradients (blue) versus a baseline condition (orange) across six dimensions: Overall Quality, Evidence Quantity, Evidence Specificity, Effect Specificity, Faithfulness, and Factual Correctness.