人物角色制图：在权重空间中描绘语言模型的个性特征
Persona Cartography: Charting Language Model Personality Traits in Weight Space
AI总结：
研究大型语言模型人物角色，用大五人格框架将其视为行为特征空间位置，训练低秩适配器控制特征，评估其效果，发现可构建混合人物角色并保持性能，还表明特征轴影响安全行为，引入无监督管道，为人格测量等架桥。
摘要：
大型语言模型展现出反复出现的行为模式——人物角色，这影响着泛化和安全性，但我们缺乏可靠工具来分解、衡量和控制它们。我们的核心见解是将人物角色视为行为特征空间中的位置，用大五人格框架描述模型人物角色。我们训练低秩适配器来增强或抑制个体特征，并通过与人工验证面板校准的语言模型评判器、特定特征的多项选择基准和标准能力评估来评估其效果。在三个系列的六个模型中，我们发现每个适配器在很大程度上随规模单调移动其目标特征，与其他适配器大致相加组合以构建混合人物角色，并在中等规模下保持能力基准上的性能。我们进一步表明，诱导的特征轴会影响下游评估中与安全相关的行为。我们还引入了一种无监督心理测量管道，从模型输出中恢复四个可解释的行为因素。人物角色控制可从权重空间中特征的学习、扩展和组合方面来考虑，为人格测量、模型编辑和安全之间架起桥梁。
英文摘要：
Large language models exhibit recurring behavioural patterns -- personas -- that shape generalisation and safety, but we lack reliable tools for decomposing, measuring, and controlling them. Our central insight is to treat personas as positions in a space of behavioural traits, using the OCEAN framework to describe model personas in terms of Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. We train low-rank adapters to amplify or suppress individual traits, and evaluate their effects using an LLM-judge calibrated against a human-validated panel, trait-specific multiple-choice benchmarks, and standard capability evaluations. Across six models from three families (4B-32B), we find that each adapter moves its target trait largely monotonically with scale, combines approximately additively with other adapters to construct mixed personas, and preserves performance on capability benchmarks at moderate scales. We further show that the induced trait axes affect safety-relevant behaviour in downstream evaluations: for example, moving along neuroticism and agreeableness axes affects frustration and sycophancy respectively. We also introduce an unsupervised psychometric pipeline that recovers four interpretable behavioural factors (tone, initiative, didacticism, epistemic caution) from model rollouts. Persona control can then be considered in terms of learning, scaling, and composing traits in weight space, providing a bridge between personality measurement, model editing, and safety.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
