Fine-Tuning Large Language Models for Automatic Detection of Sexually Explicit Content in Spanish-Language Song Lyrics

TL;DR

This paper demonstrates that fine-tuning a GPT model on a small, curated dataset effectively detects sexually explicit Spanish-language song lyrics, outperforming generic models and supporting automated content moderation and policy development.

Contribution

It introduces a domain-specific fine-tuning approach for large language models to identify explicit lyrics in Spanish music, incorporating cultural and linguistic nuances.

Findings

Achieved 87% accuracy, 100% precision and specificity in detection.

Model agreement with human experts increased to 59.2%.

Supports automated moderation and policy proposals for music content.

Abstract

The proliferation of sexually explicit content in popular music genres such as reggaeton and trap, consumed predominantly by young audiences, has raised significant societal concern regarding the exposure of minors to potentially harmful lyrical material. This paper presents an approach to the automatic detection of sexually explicit content in Spanish-language song lyrics by fine-tuning a Generative Pre-trained Transformer (GPT) model on a curated corpus of 100 songs, evenly divided between expert-labeled explicit and non-explicit categories. The proposed methodology leverages transfer learning to adapt the pre-trained model to the idiosyncratic linguistic features of urban Latin music, including slang, metaphors, and culturally specific double entendres that evade conventional dictionary-based filtering systems. Experimental evaluation on held-out test sets demonstrates that the fine-tuned model achieves 87% accuracy, 100% precision, and 100% specificity after a feedback-driven refinement loop, outperforming both its pre-feedback configuration and a non-customized baseline ChatGPT model. A comparative analysis reveals that the fine-tuned model agrees with expert human classification in 59.2% of cases versus 55.1% for the standard model, confirming that domain-specific adaptation enhances sensitivity to implicit and culturally embedded sexual references. These findings support the viability of deploying fine-tuned large language models as automated content moderation tools on music streaming platforms. Building on these technical results, the paper develops a public policy proposal for a multi-tier age-based content rating system for music analogous to the PEGI system for video games analyzed through the PESTEL framework and Kingdon's Multiple Streams Framework, establishing both the technological feasibility and the policy pathway for systematic music content regulation.