Grading the Unannotated: Insights from the WorkRB Skill Extraction Challenge at RecSys-HR 2026
Paper recorded by Signals 4 on 2026-11-02 in cs.AI. Abstract reproduced from arXiv; link to the original below.
Published 2026-11-02 on arXiv · recorded by Signals 4 on 2026-11-03
Category: cs.AI · 人工智能 · first seen 2026-11-03
Abstract
Skill extraction benchmarks score models against a few human-annotated positives and treat every other skill in the vocabulary as equally wrong. At the scale of ESCO (13,939 skills) this binary view can reward models that rank the annotated positive high for the wrong reason, and hides semantically nonsensical predictions. The WorkRB Challenge at RecSys-HR 2026 replaces it with graded relevance: e
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Cite this page: Grading the Unannotated: Insights from the WorkRB Skill Extraction Challenge at RecSys-HR 2026: the #40 most recent of 920 cs.AI papers we have recorded (as of 2026-11-02). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/grading-the-unannotated-insights-from-the-workrb-skill-extraction-challenge-at-r.html
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