arXiv:2606.29273v1 Announce Type: cross Abstract: 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 tha
The proliferation of LLMs creates new opportunities and challenges for data annotation, prompting research into human-AI collaborative methods.
Improving AI's ability to interpret nuanced human expression like song lyrics can enhance content understanding, personalization, and creative applications.
New methodologies for integrating human and LLM strengths in complex annotation tasks are emerging, paving the way for more sophisticated data labeling and model training.
- · AI developers
- · Content creators
- · Music industry
- · Data annotation companies
- · Companies relying solely on manual annotation for complex tasks
The development of more accurate and scalable song lyric annotation tools becomes feasible.
This improved annotation could lead to more emotionally intelligent AI systems capable of deeper cultural understanding.
Future AI applications might generate highly personalized and emotionally resonant content, blurring lines between human and machine creativity.
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Read at arXiv cs.AI