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

TL;DR

This paper introduces a new dataset of Reddit music requests annotated for user intent behind descriptors and evaluates how well large language models can extract these preferences, highlighting their strengths and limitations.

Contribution

It provides a manually annotated corpus for music query intent and assesses LLM capabilities in extracting preference-bearing descriptors, advancing music understanding research.

Findings

LLMs reliably extract explicit descriptors

LLMs struggle with context-dependent descriptors

The dataset serves as a benchmark for future research

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.