← 研踪 PaperTrailarXiv:2611.04260 · v1
Neologism Inoculation Prompting
首次提交 2026-11-03 11:52
版本更新 2026-11-03 11:52
雷达观测 2026-11-04 23:40
摘要
Inoculation prompting is a method to reduce undesired behaviours such as reward hacking in frontier models. It works by training a model on harmful examples under a prompt that specifies the undesired behaviour, which reduces the model's tendency to exhibit it unless prompted. However, the inoculation prompt is written in plain English, so any user can re-elicit the behaviour easily. We introduce neologism inoculation prompting (NIP), which replaces the English prompt with a new token that is trained on the frozen model and removed at deployment. NIP outperforms other inoculation methods, and performs competitively with gradient routing despite being simpler. It also works on reward hacking, but fails on sycophancy and harassment. Using control tokens and analyses of activations, we identify the mechanism for this as NIP hiding the harmful behaviour behind an unreachable region of model inputs, rather than deleting it. More broadly, our work suggests that neologisms can be a powerful way to carve out and shape access to behaviours that are undesired or harmful.
作者备注：37 pages, 20 figures, 21 tables
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v1 · 2026-11-03 11:52 · 最新观测版本
Neologism Inoculation Prompting
Inoculation prompting is a method to reduce undesired behaviours such as reward hacking in frontier models. It works by training a model on harmful examples under a prompt that specifies the undesired behaviour, which reduces the model's tendency to exhibit it unless prompted. However, the inoculation prompt is written in plain English, so any user can re-elicit the behaviour easily. We introduce neologism inoculation prompting (NIP), which replaces the English prompt with a new token that is trained on the frozen model and removed at deployment. NIP outperforms other inoculation methods, and performs competitively with gradient routing despite being simpler. It also works on reward hacking, but fails on sycophancy and harassment. Using control tokens and analyses of activations, we identify the mechanism for this as NIP hiding the harmful behaviour behind an unreachable region of model inputs, rather than deleting it. More broadly, our work suggests that neologisms can be a powerful way to carve out and shape access to behaviours that are undesired or harmful.
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