让大模型性格控制像调音量一样精细，支持连续调节
Fusian: Multi-LoRA Fusion for Fine-Grained Continuous MBTI Personality Control in Large Language Models
- 用多LoRA适配器记录性格变化轨迹，构建连续人格空间
- 通过强化学习动态融合适配器，精准匹配用户指定的性格强度
- 适用于需要细腻人格调控的对话系统、虚拟角色等场景
大型语言模型在模拟人类行为与性格方面表现出色。然而，现有的性格控制方法（如提示工程和标准监督微调）通常将性格特征视为离散类别（例如“外向”对“内向”），无法精确控制特质强度的连续变化。本文提出Fusian框架，实现大模型中细粒度、连续的性格控制。该框架分为两阶段：(1) 轨迹采集阶段，通过保存微调过程中一系列LoRA适配器，捕捉性格演变过程，有效映射出特质的连续流形；(2) 基于强化学习的动态融合阶段，训练策略网络以动态计算这些冻结适配器的混合权重。通过从策略网络参数化的狄利克雷分布中采样，Fusian融合多个适配器，使模型输出与特定数值目标强度对齐。在Qwen3-14B模型上的实验表明，Fusian在性格控制精度上显著优于基线方法，能更准确地匹配用户指定的性格强度。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have demonstrated impressive capabilities in simulating diverse human behaviors and personalities. However, existing methods for personality control, which include prompt engineering and standard Supervised Fine-Tuning (SFT), typically treat personality traits as discrete categories (e.g., "Extroverted" vs. "Introverted"), lacking the ability to precisely control the intensity of a trait on a continuous spectrum. In this paper, we introduce Fusian, a novel framework for fine-grained, continuous personality control in LLMs. Fusian operates in two stages: (1) Trajectory Collection, where we capture the dynamic evolution of personality adoption during SFT by saving a sequence of LoRA adapters, effectively mapping the continuous manifold of a trait; and (2) RL-based Dynamic Fusion, where we train a policy network using Reinforcement Learning to dynamically compute mixing weights for these frozen adapters. By sampling from a Dirichlet distribution parameterized by the policy network, Fusian fuses multiple adapters to align the model's output with a specific numerical target intensity. Experiments on the Qwen3-14B model demonstrate that Fusian achieves high precision in personality control, significantly outperforming baseline methods in aligning with user-specified trait intensities.
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