IROTE: Human-like Traits Elicitation of Large Language Model via In-Context Self-Reflective Optimization

TL;DR

This paper introduces IROTE, a novel in-context method that uses self-reflective prompts to reliably elicit human-like traits in large language models across various tasks, without fine-tuning.

Contribution

The paper proposes a new in-context trait elicitation technique based on self-reflection, improving stability and transferability of trait embodiment in LLMs.

Findings

IROTE achieves stable trait impersonation across multiple tasks.

It outperforms existing baseline methods in trait elicitation.

The method does not require fine-tuning, only prompt optimization.

Abstract

Trained on various human-authored corpora, Large Language Models (LLMs) have demonstrated a certain capability of reflecting specific human-like traits (e.g., personality or values) by prompting, benefiting applications like personalized LLMs and social simulations. However, existing methods suffer from the superficial elicitation problem: LLMs can only be steered to mimic shallow and unstable stylistic patterns, failing to embody the desired traits precisely and consistently across diverse tasks like humans. To address this challenge, we propose IROTE, a novel in-context method for stable and transferable trait elicitation. Drawing on psychological theories suggesting that traits are formed through identity-related reflection, our method automatically generates and optimizes a textual self-reflection within prompts, which comprises self-perceived experience, to stimulate LLMs' trait-driven behavior. The optimization is performed by iteratively maximizing an information-theoretic objective that enhances the connections between LLMs' behavior and the target trait, while reducing noisy redundancy in reflection without any fine-tuning, leading to evocative and compact trait reflection. Extensive experiments across three human trait systems manifest that one single IROTE-generated self-reflection can induce LLMs' stable impersonation of the target trait across diverse downstream tasks beyond simple questionnaire answering, consistently outperforming existing strong baselines.