用融合人格向量让单模型实现多智能体创意生成
BILLY: Steering Large Language Models via Merging Persona Vectors for Creative Generation
- 直接在模型激活空间提取并混合多个角色向量
- 在多项创意评测中超越单模型与多模型方法
- 无需训练，适合追求高效可控创作的场景
多大模型系统通过模拟人类集体智慧提升语言模型的创造力，但存在计算成本高、推理延迟大的问题。为此，我们提出BILLY（BlendIng persona vectors for Large Language model creativitY），一种无需训练的框架，将多模型协作的优势——多样视角与专业能力——整合进单一模型。BILLY通过直接在模型激活空间提取并融合多个独立的人格向量，在推理时使用该混合向量引导生成，实现多视角输出而无需显式通信。在多项创意导向基准测试中，BILLY优于单模型提示和传统多模型方法，同时显著降低推理时间和计算开销。分析表明，不同人格向量可融合以有效控制生成的互补维度，并增强可解释性。
原文摘要 · Abstract (English)
Multi-LLM systems enhance the creativity of large language models by simulating human collective intelligence but suffer from significant drawbacks, such as high computational costs and inference latency. To address these limitations, we propose BILLY (BlendIng persona vectors for Large Language model creativitY), a training-free framework that captures the benefits of multi-LLM collaboration, i.e. inducing diverse perspectives and specialized expertise, within a single model. BILLY operates by extracting and blending multiple distinct persona vectors directly in the model's activation space. We steer the model's generation process with this merged vector while inference, enabling multi-perspective output without explicit multi-LLM communication. Our experiments across creativity-oriented benchmarks demonstrate that BILLY surpasses single model prompting and traditional multi-LLM approaches, while substantially reducing inference time and computational costs. Our analyses further reveal that distinct persona vectors can be blended to achieve both effective control over complementary aspects of generation and greater interpretability.
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