9 papers
Floor, Ceiling, and the Fusion Gap: How Much of Crowd Reading Attention Can Machines Predict?
Kazuki Nakayashiki, Keisuke Watanabe
A benchmark score means nothing without knowing what a trivial method achieves and what the best possible method could achieve. We construct both bounds for a task with a rare kind…
Language Models Agree With Each Other, Not With Readers
Kazuki Nakayashiki, Keisuke Watanabe
Claims that language models homogenise are usually measured against human judgements collected for the study, which makes the human side an artifact of the design: a crowdworker gi…
Measuring Alignment With Reader Highlights Net of Position and Length
Kazuki Nakayashiki, Keisuke Watanabe
Context compression discards most of a document before a language model reads it, and is normally evaluated by downstream task accuracy - which makes another model the judge of wha…
Trait, Not State: The Durability of Reading Identity in Social Highlighting
Kazuki Nakayashiki, Keisuke Watanabe
Prior work on a social web highlighter located individuality in selection -- which documents a person chooses to highlight -- but measured it cross-sectionally. We ask the temporal…
The Long Tail, Not the Front Page: Cold-Start Prediction of Crowd Highlight Salience
Kazuki Nakayashiki, Keisuke Watanabe
A social highlighter's most useful signal -- which passages a crowd of readers marks -- exists only for documents people have already read. Can the aggregate crowd salience of a do…
Factions Within, Uncertain Across: Within-Document Reader Sub-Groups in Social Highlighting
Kazuki Nakayashiki, Keisuke Watanabe
When many people highlight the same document, is the crowd a single consensus, or is it internally structured into reader sub-groups that mark different things -- and is that struc…