Large language models have shown strong potential as role-playing agents for real individuals, yet faithful impersonating remains challenging.
Existing in-context learning-based methods fail to capture how individuals react under different situations.
In addition, LLM-based evaluation is difficult for obscure individuals.
To address these challenges
We propose Situation--Internal state--Behavior Persona method to incorporate situation-dependent behavioral strategies.
We further design an evaluation protocol that provides LLM evaluators with references about the impersonated individual.
We evaluate our approach on a newly constructed dataset for the task of generating replies on social media.
Experimental results show that our proposed method outperforms state-of-the-art ICL-based baselines, while our evaluation protocol achieves moderate correlation with human judgment.
Besides, experiments on fictional-character benchmarks demonstrate that our proposed method is applicable beyond the social media setting.
These findings suggest that incorporating behavioral information broadly improves the fidelity of role-playing for real individuals on social media or fictional characters.