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

大型语言模型通过上下文学习提升音乐情感预测能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-23 04:00

研究人员探索了利用大型语言模型（LLM）的上下文学习（ICL）来改进音乐情感效价（valence）和唤醒度（arousal）的预测。虽然唤醒度通常可以从音频中预测，但效价更具主观性和情境依赖性。研究发现，ICL，特别是使用歌词和音频字幕等文本数据时，显著提高了效价预测的准确性。结合原始音频的多模态方法也显示出潜力，这表明ICL是当前语言模型在预测音乐情感方面一种有效的推理时策略。 AI

影响展示了LLM在超越传统任务的细微、主观分析方面的潜力，为AI在创意领域开辟了道路。

排序理由 arXiv上发表的研究论文，详细介绍了使用LLM预测音乐情感的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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本文如何被排名

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Newsworthiness bucket: Tool; arXiv上发表的研究论文，详细介绍了使用LLM预测音乐情感的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.CL TIER_1 English(EN) · Chi-En Dai, Chih-Pin Tan, Yi-Hsuan Yang · 2026-10-23 04:00

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

arXiv:2610.31202v1 Announce Type: cross 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) w…

本文如何被排名

报道来源 [1]

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

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