激活空间人格引导：用于LLMs中稳定特质控制的混合层选择
Activation-Space Personality Steering: Hybrid Layer Selection for Stable Trait Control in LLMs
AI总结：
本文提出通过混合层选择方法，在LLMs中实现稳定的人格控制，利用Big Five人格特质构建低秩子空间，以提升模型输出的可控性与实用性。
摘要：
大型语言模型在其生成过程中表现出隐含的人格特征，但可靠地控制或对齐这些特性以满足特定需求仍是一个开放性挑战。文献中需要填补的关键空白是，在生成过程中有效机制的行为操控需求。具有人格意识的LLMs朝着这一目标提供了一个有前途的方向。然而，这些心理构念与在LLMs中的表示之间的关系仍处于探索阶段，需要进一步研究。此外，理解并研究这些表示用于引导模型行为也是引人入胜的。我们提出了一种新的流程，通过使用Big Five人格特质（开放性、尽责性、外向性、宜人性和神经质）从Transformer层中提取隐藏状态激活，这是一种全面且经过实证验证的框架，用于建模人类人格，应用低秩子空间发现方法，并在不同的模型架构中识别出特定特质的最优层，以实现稳健的注入。最终的人格对齐方向通过具有动态层选择的灵活引导框架进行操作化，使LLM输出中的特质表达能够得到精确控制。我们的发现表明，人格特质占据一个低秩共享子空间，这些潜在结构可以通过精心的扰动转换为有效的引导机制，而不会影响流畅性、方差和通用能力，有助于弥合心理理论与实际模型对齐之间的差距。
英文摘要：
Large Language Models exhibit implicit personalities in their generation, but reliably controlling or aligning these traits to meet specific needs remains an open challenge. The need for effective mechanisms for behavioural manipulation of the model during generation is a critical gap in the literature that needs to be fulfilled. Personality-aware LLMs hold a promising direction towards this objective. However, the relationship between these psychological constructs and their representations within LLMs remains underexplored and requires further investigation. Moreover, it is intriguing to understand and study the use of these representations to steer the models' behaviour. We propose a novel pipeline that extracts hidden state activations from transformer layers using the Big Five Personality Traits (Openness, Conscientiousness, Extraversion, Agreeableness and Neuroticism), which is a comprehensive and empirically validated framework to model human personality applies low-rank subspace discovery methods, and identifies trait-specific optimal layers across different model architectures for robust injection. The resulting personality-aligned directions are then operationalised through a flexible steering framework with dynamic layer selection, enabling precise control of trait expression in LLM outputs. Our findings reveal that personality traits occupy a low-rank shared subspace, and that these latent structures can be transformed into actionable mechanisms for effective steering through careful perturbations without impacting the fluency, variance and general capabilities, helping to bridge the gap between psychological theory and practical model alignment.
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。
