From Novice to Expert: Generating Audience-Dependent Concert Moderations with RAG-LLMs

AbstractIn this paper, we study the capabilities of large language models (LLMs) to adapt a concert moderation to diverse expertise levels of listeners. Our proof-of-concept concert moderator is based on retrieval-augmented generation (RAG) and uses few-shot audience modelling to infer listener’s expertise. We study the capabilities of the system to adapt to three different listener’s expertise levels. Two open domain LLMs are compared: gpt-oss:20b and llama3. The recognised differences among the models suggest that they vary in how directly they reproduce versus paraphrase retrieved information while maintaining semantic alignment.