Abstract
Recently, Large language models (LLMs) have shown great promise across a diversity of tasks, ranging from generating images to reasoning spatially. Considering their remarkable (and growing) textual reasoning capabilities, we investigate LLMs' potency in conducting analyses of an individual's preferences in music (based on playlist metadata, personal write-ups, etc.) and producing effective prompts (based on these analyses) to be passed to Suno AI (a generative AI tool for music production). Our proposition of a novel LLM-based textual representation to music model (which we call TuneGenie) and the various methods we develop to evaluate & benchmark similar models add to the increasing (and increasingly controversial) corpus of research on the use of AI in generating art.
Abstract (translated)
最近，大型语言模型（LLM）在多种任务中展现了巨大的潜力，从生成图像到进行空间推理。鉴于它们卓越且不断增长的文本推理能力，我们研究了这些模型在分析个人音乐偏好方面的效力（基于播放列表元数据、个人评论等），并开发有效提示（基于这些分析）以传递给Suno AI（一种用于音乐制作的生成式AI工具）。我们提出了一种新颖的LLM基文本表示到音乐模型（称之为TuneGenie），以及我们为评估和基准测试类似模型而发展的各种方法，都增加了关于在艺术创作中使用AI的研究日益增长且日益具有争议性的文献。
URL
https://arxiv.org/abs/2506.12083
