arXiv cs.AI· Robert Graham, Yariv Barsheshat, Phil Blandfort, Sabri Alouache·· 9 天前AI 评分29
模型一致性研究：窄域微调模型在重采样下自相矛盾
An Investigation of Model Coherence: Narrow Finetunes Contradict Themselves Under Resampling
AI 导读
研究提出用175个问题测量模型一致性，通过重采样同一问题的答案检测矛盾，该方法特异性高，仅在问题明显时才判定模型不一致。结果显示窄域微调模型得分较差，文献中的模型存在身份混淆、内省失败和合理化等严重问题。
正文 · 原文
Abstract:A large body of research measures model coherence based on output variance without adequately considering competing causes. We identify two such causes, ambiguity and indifference, and we introduce a set of 175 questions where contradicting answers cannot easily be explained by either. We then measure incoherence in terms of contradictions when resampling answers to the same question. In contrast to other methods our metric has high specificity, and only ranks models as incoherent when the issues are glaring. Even so, we find narrow finetunes score poorly. Inspecting inconsistencies flagged by our method, we find that model organisms from the literature display severe issues such as identity conflation, introspection failures and rationalizations. These findings suggest that the pathologies induced by narrow finetuning may limit what these models can tell us about coherent misaligned behaviour.
| Comments: | 18 pages, 5 figures, 4 tables. Code and dataset: this https URL |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.12129 [cs.AI] |
| (or arXiv:2610.12129v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12129 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yariv Barsheshat [view email]
[v1]
Thu, 8 Oct 2026 15:22:00 UTC (321 KB)
来源：arXiv cs.AI · arxiv.org