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人格泛化可预测吗?

原文标题 · Is Persona Generalization Predictable?

作者: Olina Mukherjee, Arnav Goel, Aditi Raghunathan | 人物泛化现象发生于对语言模型在某一领域进行微调时在其他领域引发行为变化——例如,在新兴失配中观察到的广泛不安全行为。我们发现了一个令人惊讶的“在线”现象于人物泛化中:不同微调超参数下的域内与域外人物率遵循一种近似概率线性关系

来源:arXiv cs.AI/CL/LG · arxiv.org

正文为英文,可一键机器翻译(仅首次需要等待)

Computer Science > Computation and Language

Title: Is Persona Generalization Predictable?

Abstract: Persona generalization occurs when fine-tuning a language model in one domain induces behavioral changes in others -- for example, the broadly unsafe behavior observed in emergent misalignment. We discover a surprising "on-the-line" phenomenon in persona generalization: in-domain and out-of-domain persona rates across various fine-tuning hyperparameters follow an approximately probit-linear relationship, and the specific probit-linear relationship is surprisingly shared even across model families and model sizes, including Qwen and Llama. Training choices that leave the set of trainable parameters unchanged, such as learning rate, weight decay, and training duration, largely move models along the same line, reducing transfer mainly by weakening in-domain learning. Changing which layers are fine-tuned, however, can change the slope. For unsafe behavior, fine-tuning only later layers yields shallower slopes, retaining strong in-domain learning while reducing out-of-domain misalignment. This "on-the-line" phenomenon unifies unexpected, isolated observations of persona generalization within a quantitative framework for predicting and controlling behavioral transfer.

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