# Music Discovery Dialogue Generation Using Human Intent Analysis and Large Language Models

arXiv: https://arxiv.org/abs/2411.07439 · HTML: https://mlsanity.com/paper/2411.07439/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2024.

## Built on by (most cited first, 4 of 4)

- [Just Ask for Music (JAM): Multimodal and Personalized Natural Language Music Recommendation](https://mlsanity.com/paper/2507.15826/) (2025)
- [TalkPlayData 2: An Agentic Synthetic Data Pipeline for Multimodal Conversational Music Recommendation](https://mlsanity.com/paper/2509.09685/) (2025)
- [Predicting User Intents and Musical Attributes from Music Discovery Conversations](https://mlsanity.com/paper/2411.12254/) (2024)
- [Mental Damage: Caption Poisoning Attacks on Retrieval-Augmented Text-to-Music Generation](https://mlsanity.com/paper/2605.30365/) (2026)

## Abstract (arXiv)

A conversational music retrieval system can help users discover music that matches their preferences through dialogue. To achieve this, a conversational music retrieval system should seamlessly engage in multi-turn conversation by 1) understanding user queries and 2) responding with natural language and retrieved music. A straightforward solution would be a data-driven approach utilizing such conversation logs. However, few datasets are available for the research and are limited in terms of volume and quality. In this paper, we present a data generation framework for rich music discovery dialogue using a large language model (LLM) and user intents, system actions, and musical attributes. This is done by i) dialogue intent analysis using grounded theory, ii) generating attribute sequences via cascading database filtering, and iii) generating utterances using large language models. By applying this framework to the Million Song dataset, we create LP-MusicDialog, a Large Language Model based Pseudo Music Dialogue dataset, containing over 288k music conversations using more than 319k music items. Our evaluation shows that the synthetic dataset is competitive with an existing, small human dialogue dataset in terms of dialogue consistency, item relevance, and naturalness. Furthermore, using the dataset, we train a conversational music retrieval model and show promising results.