# Enhancing MusicGen with Prompt Tuning
## 6 Conclusions  
The proposed method addresses the challenges of generating music for underrepresented genres by enhancing MusicGen through prompt tuning. It demonstrates its effectiveness in improving the adaptability and quality of music generation while maintaining efficiency. By fine-tuning only a small set of parameters, it preserves the integrity of the traditional MusicGen, highlighting the potential of the PEFT technique in optimizing large-scale generative models.  
The findings illustrate that PEFT techniques, such as prompt tuning, can be successfully applied to complex models like MusicGen, enabling targeted improvements without extensive computational resources. The proposed method highlights a practical pathway for advancing generative AI systems across various domains, providing scalability and versatility in handling diverse creative tasks. By exploring the integration of the lightweight fine-tuning technique, the proposed method enables the adaptation of sophisticated AI models to specialized applications.  
This paper highlights several avenues for future research that could advance generative music models in the following focused directions:  
* Enhanced Evaluation Methodologies: Although CLAP and KLD provide objective benchmarks, they do not fully capture musical expressiveness (e.g., emotional nuance, creativity, cultural context). Developing music-specific evaluation frameworks that integrate both objective and subjective criteria remains a pressing need.
* Broader Parameter-Efficient Adaptation: Beyond prompt tuning, techniques such as meta-learning, contrastive learning, and reinforcement learning could further improve adaptability to rare genres and complex styles while preserving computational efficiency.
* Expanded Conditioning Modalities: Extending input beyond text or audio--e.g., images, sketches, or symbolic scores--would enable more intuitive, cross-modal interaction with generative systems and broaden their creative applications.  
**Author Contributions:** Conceptualization, H.S., J.I. and Y.S.; methodology, H.S., J.I. and Y.S.; software, H.S.; validation, H.S.; formal analysis, H.S. and J.I.; investigation, H.S. and J.I.; writing--original draft preparation, H.S. and J.I.; writing--review and editing, H.S., J.I. and Y.S.; visualization, H.S. and J.I.; supervision, Y.S.; project administration, H.S. and J.I. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Institute of Information & communications Technology Planning & Evaluation (IITP) under the Artificial Intelligence Convergence Innovation Human Resources Development (IITP-2025-RS-2023-00254592) grant funded by the Korean government (MSIT). This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2021R1F1A1063466).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Restrictions apply to the availability of these data. Data were obtained from AI Hub and are available AI Hub with the permission of AI Hub ([[https://www.aihub.or.kr/](https://www.aihub.or.kr/)]([https://www.aihub.or.kr/](https://www.aihub.or.kr/)), accessed on 1 July 2025).  
**Conflicts of Interest:** The authors declare no conflicts of interest.