Large Language Models (LLMs) are trained on massive text corpora, which are encoded with diverse personality traits. This triggers an interesting goal of eliciting a desired personality trait from the LLM, and probing its behavioral preferences. Accordingly, we formalize the persona elicitation task, aiming to customize LLM behaviors to align with a target persona. We present Persona In-Context Learning (PICLe), a novel persona elicitation framework grounded in Bayesian inference. At the core, PICLe introduces a new ICL example selection criterion based on likelihood ratio, which is designed to optimally guide the model in eliciting a specific target persona. We demonstrate the effectiveness of PICLe through extensive comparisons against baseline methods across three contemporary LLMs. Code is available at https://github.com/deeplearning-wisc/picle.

翻译：大语言模型（LLMs）在包含多样化人格特质的海量文本语料上进行训练。这引出了一个有趣的目标：从LLM中引出所需的人格特质，并探索其行为偏好。为此，我们形式化定义了角色引出任务，旨在定制LLM的行为以匹配目标角色。我们提出基于角色上下文学习（PICLe）——一种基于贝叶斯推断的新型角色引出框架。其核心是，PICLe引入了一种基于似然比的新上下文学习示例选择准则，该准则旨在最优引导模型引出特定目标角色。通过跨三种当代LLM与基线方法的广泛对比，我们证明了PICLe的有效性。代码已开源在https://github.com/deeplearning-wisc/picle。