Beyond Musical Descriptors: Extracting Preference-Bearing Intent in Music Queries
arXiv:2602.12301v1 Announce Type: cross Abstract: Although annotated music descriptor datasets for user queries are increasingly common, few consider the user's intent behind these descriptors, which is essential for effectively meeting their needs. We introduce MusicRecoIntent, a manually annotated corpus of 2,291 Reddit music requests, labeling musical descriptors across seven categories with positive, negative, or referential preference-bearing roles. We then investigate how reliably large language models (LLMs) can extract these music descriptors, finding that they do capture explicit descriptors but struggle with context-dependent ones. This work can further serve as a benchmark for fine-grained modeling of user intent and for gaining insights into improving LLM-based music understanding systems.
arXiv:2602.12301v1 Announce Type: cross Abstract: Although annotated music descriptor datasets for user queries are increasingly common, few consider the user's intent behind these descriptors, which is essential for effectively meeting their needs. We introduce MusicRecoIntent, a manually annotated corpus of 2,291 Reddit music requests, labeling musical descriptors across seven categories with positive, negative, or referential preference-bearing roles. We then investigate how reliably large language models (LLMs) can extract these music descriptors, finding that they do capture explicit descriptors but struggle with context-dependent ones. This work can further serve as a benchmark for fine-grained modeling of user intent and for gaining insights into improving LLM-based music understanding systems.
Executive Summary
The article 'Beyond Musical Descriptors: Extracting Preference-Bearing Intent in Music Queries' introduces MusicRecoIntent, a manually annotated corpus of 2,291 Reddit music requests. This corpus labels musical descriptors across seven categories with positive, negative, or referential preference-bearing roles. The study investigates the reliability of large language models (LLMs) in extracting these descriptors, finding that while LLMs can capture explicit descriptors, they struggle with context-dependent ones. The work serves as a benchmark for fine-grained modeling of user intent and improving LLM-based music understanding systems.
Key Points
- ▸ Introduction of MusicRecoIntent corpus with 2,291 annotated Reddit music requests.
- ▸ Labeling of musical descriptors into seven categories with preference-bearing roles.
- ▸ Investigation of LLM reliability in extracting music descriptors.
- ▸ LLMs capture explicit descriptors but struggle with context-dependent ones.
- ▸ Benchmark for fine-grained modeling of user intent and improving music understanding systems.
Merits
Comprehensive Dataset
The creation of MusicRecoIntent provides a valuable resource for researchers, offering a detailed and manually annotated dataset that can be used to improve music recommendation systems.
Insightful Analysis
The study provides a nuanced understanding of how LLMs perform in extracting musical descriptors, highlighting both strengths and limitations.
Benchmark for Future Research
The work sets a benchmark for future research in fine-grained modeling of user intent and improving LLM-based music understanding systems.
Demerits
Limited Scope of Data
The dataset is derived solely from Reddit, which may not be representative of all user queries and preferences.
Context-Dependent Descriptor Limitations
The study acknowledges that LLMs struggle with context-dependent descriptors, which is a significant limitation in real-world applications.
Generalizability
The findings may not be generalizable to other platforms or types of music queries, limiting the broader applicability of the results.
Expert Commentary
The article presents a significant advancement in the field of music recommendation systems by introducing the MusicRecoIntent corpus and investigating the capabilities of LLMs in extracting preference-bearing intent from music queries. The comprehensive dataset and insightful analysis provide a robust foundation for future research. However, the study's limitations, particularly the struggle with context-dependent descriptors and the limited scope of the dataset, highlight areas for further exploration. The implications of this work are far-reaching, with potential applications in improving music recommendation algorithms and enhancing user experience. The study also underscores the importance of ethical considerations in designing systems that accurately represent user preferences. Overall, this work is a valuable contribution to the intersection of NLP and music understanding, offering both practical and theoretical insights.
Recommendations
- ✓ Expand the dataset to include a more diverse range of music queries from various platforms to improve generalizability.
- ✓ Further investigate methods to enhance LLM performance in extracting context-dependent descriptors to address current limitations.