Predicting Music Valence and Arousal via Textual and Multimodal In-Context Learning in Large Language Models

Chi-En Dai, Chih-Pin Tan, Yi-Hsuan Yang · 2026 · arXiv

DOI: 10.48550/arXiv.2610.31202 · open-access PDF

Abstract

While arousal can be reliably predicted from musical audio, emotional valence remains difficult to model due to its subjective and context-dependent nature. In this work, we investigate the potential of in-context learning (ICL) with large language models to advance continuous valence and arousal prediction. Rather than predicting emotion scores directly, our framework leverages contextual demonstrations from an auxiliary dataset to refine the initial predictions produced by a small, task-specific base model. We systematically evaluate a text-based ICL variant using lyrics and generated audio

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