打破同质性：多样化角色设定以提升大语言模型的创造性输出

中文摘要 AI 辅助

语言模型在面对开放式任务时往往会产生同质化的响应；这种同质性可能引发群体思维——即思想趋同于单一且可能次优的决策。我们将角色多样化形式化为一个集合层面的条件化问题，并研究两个正交的设计选择：选择角色与生成角色，以及空间填充型多样性与前沿搜寻型多样性。我们用四种方法实例化这一设计空间，涵盖覆盖性子集选择、离散性子集选择、均匀覆盖采样以及进化式角色生成。在替代用途任务（AUT）、Infinity-Chat和发散联想任务（DAT）上的评估显示了所提方法在各类任务和创造性目标上的优势。在AUT上，与仅使用任务提示相比，进化式角色生成将响应多样性提高了78.8%，原创性提高了26.1%，灵活性提高了49.5%，整体创造性提高了13.9%，同时保持了98.5%的有效性；在Infinity-Chat上，相对于随机角色，它使角色诱导的响应分离度几乎翻倍。此外，进化式角色与创造性优化提示相结合，进一步将其响应多样性提高了18.6%，创造性提高了6.3%。这些结果确立了角色集合几何结构作为一种与任务无关的机制，用于激发大语言模型的发散性输出，并支持将角色多样化作为提示优化的可复用补充手段。

英文摘要

Language models often produce homogeneous responses to open-ended tasks; such homogeneity can spawn groupthink-the convergence of ideas toward a singular and potentially suboptimal decision. We formulate persona diversification as a set-level conditioning problem and study two orthogonal design choices: selecting versus generating personas, and space-filling versus frontier-seeking diversity. We instantiate this design space with four methods spanning coverage and dispersion subset selections, uniform-coverage sampling, and evolutionary persona generation. Evaluations on the Alternative Uses Task (AUT), Infinity-Chat, and Divergent Association Task (DAT) show the benefits of the proposed methods across tasks and creativity objectives. On AUT, evolutionary persona generation increases response diversity by 78.8%, originality by 26.1%, flexibility by 49.5%, and holistic creativity by 13.9% over task-only prompting, while maintaining 98.5% validity; on Infinity-Chat, it nearly doubles persona-induced response separation relative to random personas. Moreover, evolutionary personas compose with creativity-optimized prompting, further increasing its response diversity by 18.6% and creativity by 6.3%. These results establish persona-set geometry as a task-agnostic mechanism for eliciting divergent LLM outputs, and support persona diversification as a reusable complement to prompt optimization.

发表机构

• Purdue University（普渡大学）
• J.P. Morgan AI Research（摩根大通人工智能研究院）

机构由 AI 辅助整理，请以论文原文为准。