# Beyond Musical Descriptors: Extracting Preference-Bearing Intent in Music Queries

arXiv: https://arxiv.org/abs/2602.12301 · HTML: https://mlsanity.com/paper/2602.12301/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 11 Feb 2026.

## Builds on (most cited first, 11 of 11)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Toward Universal Text-to-Music Retrieval](https://mlsanity.com/paper/2211.14558/) (2022)
- [JamendoMaxCaps: A Large Scale Music-caption Dataset with Imputed Metadata](https://mlsanity.com/paper/2502.07461/) (2025)
- [Probing Pre-trained Auto-regressive Language Models for Named Entity Typing and Recognition](https://mlsanity.com/paper/2108.11857/) (2021)
- [Negation: A Pink Elephant in the Large Language Models' Room?](https://mlsanity.com/paper/2503.22395/) (2025)
- [A Human Subject Study of Named Entity Recognition (NER) in Conversational Music Recommendation Queries](https://mlsanity.com/paper/2303.06944/) (2023)
- [Just Ask for Music (JAM): Multimodal and Personalized Natural Language Music Recommendation](https://mlsanity.com/paper/2507.15826/) (2025)
- [A Benchmark and Robustness Study of In-Context-Learning with Large Language Models in Music Entity Detection](https://mlsanity.com/paper/2412.11851/) (2024)
- [Recognizing Musical Entities in User-generated Content](https://mlsanity.com/paper/1904.00648/) (2019)
- [Text2Playlist: Generating Personalized Playlists from Text on Deezer](https://mlsanity.com/paper/2501.05894/) (2025)
- [Conversational Recommendation as Retrieval: A Simple, Strong Baseline](https://mlsanity.com/paper/2305.13725/) (2023)

## Abstract (arXiv)

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.