  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.
