通过人与大模型对齐，提升歌词情感标注准确性。
A Hybrid Framework for Song Lyric Annotation Based on Human-LLM Alignment
- 构建句子级歌词情感数据集，分析人类与大模型标注差异
- 提出混合标注框架，预测并修正标注不一致问题
- 适合需要高质量歌词标注的音乐情感分析研究者
歌曲歌词的情感识别是一项挑战性任务，因为歌词情感未必与整首歌的情绪一致。因此，歌词标注仍处于探索阶段。受大语言模型（LLM）辅助标注研究的启发，我们通过创建一个新的句子级歌词数据集，考察了人类与LLM在歌词标注中的对齐情况。观察发现该任务具有高度主观性且存在内在挑战。基于此，我们提出一种混合标注框架，通过预测潜在的标注不一致，优化人类与LLM的协同标注效果。
原文摘要 · Abstract (English)
Emotion recognition of song lyrics is a challenging task since lyrics may not necessarily align with the overall emotion of a song. As a result, lyrics annotation remains largely underexplored. Drawing inspiration from research in large language model (LLM) assisted annotation, we examine the alignment between humans and LLMs for annotation of lyrics by creating a new sentence-level dataset of lyrics. Our observations highlight the subjectivity of the task and the inherent challenges. Following this, we present a hybrid annotation framework that optimizes human and LLM annotation by predicting potential misalignment in annotation.
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