More Reader Data or a Better Teacher? Updating a Model of Crowd Highlights in Late 2026
A model trained on readers' highlights can show a newcomer where earlier readers converged on a page, even on pages with few readers. A year of stronger language models suggests two upgrades: use them directly, or use them as teachers. We compare both with a third, more reader data, on one social highlighting platform, with the comparisons, the selection rules and a sealed set of fresh pages fixed in advance. On 215 development pages, seven language models released by October 2026 rank sentences zero-shot at an average precision of at most 0.3744 against the sentences half the readers marked most, and 0.3931 when fused with reading position, while a 3-billion-parameter model trained on re...
| Authors | Kazuki Nakayashiki, Keisuke Watanabe |
|---|---|
| Primary category | cs.IR |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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