用模型合并生成性格向量，实现大模型性格的连续调控
Personality Vector: Modulating Personality of Large Language Models by Model Merging
- 通过权重相减构造性格向量，实现无需训练的性格调节
- 支持多性格组合与强度连续控制，效果稳定可靠
- 适用于多种大模型，适合个性化AI系统开发
为满足个性化AI系统需求，研究者日益关注将大语言模型（LLMs）行为对齐于人类性格特征。以往方法虽有效，但难以捕捉人格的连续性与多维性。本文提出基于模型合并的性格调节新方法：通过预训练模型与特定性格微调模型的权重相减，构建性格向量。通过合并性格向量，可在不进行额外训练的情况下使模型表现出目标性格特征。大量实验表明，性格向量可实现性格强度的连续调控，并支持多性格组合。此外，性格向量在不同下游模型间具有迁移能力，表明其编码了通用的人格表征。代码已公开。
原文摘要 · Abstract (English)
Driven by the demand for personalized AI systems, there is growing interest in aligning the behavior of large language models (LLMs) with human traits such as personality. Previous attempts to induce personality in LLMs have shown promising results, but they struggle to capture the continuous and multidimensional nature of human traits. In this work, we propose a novel method for personality modulation in LLMs via model merging. Specifically, we construct personality vectors by subtracting the weights of a pre-trained model from those of the fine-tuned model on a given personality trait. By merging personality vectors, we enable LLMs to exhibit desired personality traits without additional training. Extensive experiments show that personality vectors enable continuous control over trait intensity and support the composition of multiple traits. Furthermore, personality vectors transfer across diverse downstream models, suggesting that they encode generalizable representations of personality. Our code is available at here.
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