Inferring Value Criteria from Ordinal Preferences: An Iterative In-Context Learning Framework for Music Generation

Adapting a generative music system to an individual's taste requires learning what that listener values. Listeners can rank pieces, but their underlying criteria may be tacit and difficult to articulate. We ask whether and under what conditions a large language model (LLM) can adapt symbolic music g