用大模型嵌入对齐多数据集情绪标签，实现零样本音乐情感预测。
Leveraging LLM Embeddings for Cross Dataset Label Alignment and Zero Shot Music Emotion Prediction
- 用LLM嵌入聚类情绪标签，跨数据集对齐不同标签体系。
- 引入对齐正则化，提升模型在未见数据上的泛化能力。
- 无需微调即可预测新情绪类别，适合跨域情感分析场景。
本文提出一种新型音乐情感识别方法，利用大语言模型（LLM）嵌入实现多数据集间情绪标签的对齐，并支持对新类别进行零样本预测。首先，计算情绪标签的LLM嵌入，通过非参数聚类将跨数据集的不相交标签分组；随后，将音乐特征（MERT）映射到LLM嵌入空间。为进一步增强模型性能，引入对齐正则化，使不同聚类中心对应的MERT嵌入相互分离，从而提升模型对未见数据集的适应能力。实验表明，该方法可在新数据集上实现零样本推理，有效推广至未见标签，无需额外训练。
原文摘要 · Abstract (English)
In this work, we present a novel method for music emotion recognition that leverages Large Language Model (LLM) embeddings for label alignment across multiple datasets and zero-shot prediction on novel categories. First, we compute LLM embeddings for emotion labels and apply non-parametric clustering to group similar labels, across multiple datasets containing disjoint labels. We use these cluster centers to map music features (MERT) to the LLM embedding space. To further enhance the model, we introduce an alignment regularization that enables dissociation of MERT embeddings from different clusters. This further enhances the model's ability to better adaptation to unseen datasets. We demonstrate the effectiveness of our approach by performing zero-shot inference on a new dataset, showcasing its ability to generalize to unseen labels without additional training.
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