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Lost in Translation: Why Lower Bias Scores Don't Mean Fairer Models

Paper recorded by Signals 4 on 2026-10-14 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-14 on arXiv · recorded by Signals 4 on 2026-10-15

Category: cs.CL · 自然语言处理 · first seen 2026-10-15

Abstract

Bias audits of multilingual language models usually compare a model's average stereotype score in English with its average in another language and read the difference as a difference in fairness. We ask how much of that difference is noise. Auditing ten open models in eight languages, we measure how far scores move when English test items are merely reworded, test every language shift item by item

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#244 most recent of 660 cs.CL papers we have recorded · ↑ newer: ReaDy: Recurrent States as Dynamic Context for Speculative Drafting in · ↓ older: AdaRSI: On-Demand Recursive Self-Improvement for Embodied Agents
Cite this page: Lost in Translation: Why Lower Bias Scores Don't Mean Fairer Models: the #244 most recent of 660 cs.CL papers we have recorded (as of 2026-10-14). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/lost-in-translation-why-lower-bias-scores-don-t-mean-fairer-models.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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