arXiv cs.CY· Plawan Kumar Rath·· 12 小时前AI 评分31
蒸馏小模型的弃权选项测出了什么：拒绝损失、采样抖动与选项捕获
What the Abstention Option Measures: Refusal Loss, Sampling Churn, and Option Capture in Distilled Small Language Models
AI 导读
一项测量研究指出，基准中的弃权选项会混淆三种机制：拒绝损失、参考模型自身两次运行间的采样抖动，以及学生模型只要提供弃权选项就选它的选项捕获。参考模型跨推理种子重采样时，已有 10% 到 27% 的拒绝回答变成刻板印象答案；用拒绝样例训练的学生模型在弃权永不正确科学题上最多 95% 选择弃权，并蔓延到另外三个基准。
正文
Abstract:Small instruction-tuned language models reach phones, cars, and offline assistants through knowledge distillation, and whether a distilled model is fair enough to ship is often decided by a multiple-choice bias score on a benchmark that offers an abstention option. Three mechanisms move behavior around the abstention option: refusal loss, where the student stops declining on items its pre-distillation reference declined; sampling churn, the same change observed between two runs of the reference itself; and option capture, where the student takes an abstention option whenever one is offered. Injecting each mechanism into a reference's own records shows that aggregate scores see only outright loss, per-item scores see churn and remapping, and only conditioning on committed answers stops moved refusal mass from reading as fairness. Resampling a reference across inference seeds already turns 10% to 27% of its refusals into stereotype answers, so a per-item refusal metric reported without that floor overstates damage. Option capture is what corpus repair teaches: students trained with refusal exemplars take an offered abstention option on up to 95% of science questions where abstention is never correct, and the habit carries to three further benchmarks. Free-form evaluation shows that the refusals multiple choice loses are not where free-form stereotyping occurs. We distill these findings into a per-condition report anchored to the reference, with measured floors and ceilings, and apply it to four student families against a fine-tuning control: distillation keeps fewer of the reference's refusals than fine-tuning on the same prompts where the reference's refusals are reliable, by a few points, while the common metrics report improvements whenever refusal mass moves. A bias score quoted for a distilled model should say which of these it reports.
| Comments: | 29 pages, 9 figures, 15 tables. v3: retitled (previously "The Asymmetric Effects of Knowledge Distillation on Bias in Small Language Models") and reframed as a measurement study |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY) |
| ACM classes: | I.2.7; I.2.6; K.4.1 |
| Cite as: | arXiv:2607.28639 [cs.CL] |
| (or arXiv:2607.28639v3 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2607.28639 arXiv-issued DOI via DataCite |
Submission history
From: Plawan Kumar Rath [view email]
[v1]
Tue, 19 May 2026 23:09:28 UTC (88 KB)
[v2]
Mon, 3 Aug 2026 22:51:27 UTC (96 KB)
[v3]
Sat, 24 Oct 2026 15:37:00 UTC (147 KB)
来源：arXiv cs.CY · arxiv.org