RAGs to Riches: RAG-like Few-shot Learning for Large Language Model Role-playing

• URL: http://arxiv.org/abs/2509.12168v1
• Date: Mon, 15 Sep 2025 17:31:15 GMT
• Title: RAGs to Riches: RAG-like Few-shot Learning for Large Language Model Role-playing
• Authors: Timothy Rupprecht, Enfu Nan, Arash Akbari, Arman Akbari, Lei Lu, Priyanka Maan, Sean Duffy, Pu Zhao, Yumei He, David Kaeli, Yanzhi Wang,
• Abstract summary: Large language models (LLMs) are increasingly deployed in high-stakes domains such as healthcare, education, and governance.<n>Inspired by Retrieval-Augmented Generation (RAG), we reformulate LLM role-playing into a text retrieval problem.
• Score: 26.263809408983306
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Role-playing Large language models (LLMs) are increasingly deployed in high-stakes domains such as healthcare, education, and governance, where failures can directly impact user trust and well-being. A cost effective paradigm for LLM role-playing is few-shot learning, but existing approaches often cause models to break character in unexpected and potentially harmful ways, especially when interacting with hostile users. Inspired by Retrieval-Augmented Generation (RAG), we reformulate LLM role-playing into a text retrieval problem and propose a new prompting framework called RAGs-to-Riches, which leverages curated reference demonstrations to condition LLM responses. We evaluate our framework with LLM-as-a-judge preference voting and introduce two novel token-level ROUGE metrics: Intersection over Output (IOO) to quantity how much an LLM improvises and Intersection over References (IOR) to measure few-shot demonstrations utilization rate during the evaluation tasks. When simulating interactions with a hostile user, our prompting strategy incorporates in its responses during inference an average of 35% more tokens from the reference demonstrations. As a result, across 453 role-playing interactions, our models are consistently judged as being more authentic, and remain in-character more often than zero-shot and in-context Learning (ICL) methods. Our method presents a scalable strategy for building robust, human-aligned LLM role-playing frameworks.

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