In recent years, generated content in music has gained significant
popularity, with large language models being effectively utilized to produce
human-like lyrics in various styles, themes, and linguistic structures. This
technological advancement supports artists in their creative processes but also
raises issues of authorship infringement, consumer satisfaction and content
spamming. To address these challenges, methods for detecting generated lyrics
are necessary. However, existing works have not yet focused on this specific
modality or on creative text in general regarding machine-generated content
detection methods and datasets. In response, we have curated the first dataset
of high-quality synthetic lyrics and conducted a comprehensive quantitative
evaluation of various few-shot content detection approaches, testing their
generalization capabilities and complementing this with a human evaluation. Our
best few-shot detector, based on LLM2Vec, surpasses stylistic and statistical
methods, which are shown competitive in other domains at distinguishing
human-written from machine-generated content. It also shows good generalization
capabilities to new artists and models, and effectively detects post-generation
paraphrasing. This study emphasizes the need for further research on creative
content detection, particularly in terms of generalization and scalability with
larger song catalogs. All datasets, pre-processing scripts, and code are
available publicly on GitHub and Hugging Face under the Apache 2.0 license.

通过对高质量合成歌词进行综合定量评估，我们基于 LIM2 模型构建的最佳 few-shot 检测器，在区分人工创作和机器生成的内容方面超越了样式和统计方法，并且对新艺术家和模型具有良好的泛化能力，同时有效地检测后期生成的改写。

使用少样本推理检测合成歌词

Detecting Synthetic Lyrics with Few-Shot Inference

For the ore particle size detection, obtaining a sizable amount of
high-quality ore labeled data is time-consuming and expensive. General object
detection methods often suffer from severe over-fitting with scarce labeled
data. Despite their ability to eliminate over-fitting, existing few-shot object
detectors encounter drawbacks such as slow detection speed and high memory
requirements, making them difficult to implement in a real-world deployment
scenario. To this end, we propose a lightweight and effective few-shot detector
to achieve competitive performance with general object detection with only a
few samples for ore images. First, the proposed support feature mining block
characterizes the importance of location information in support features. Next,
the relationship guidance block makes full use of support features to guide the
generation of accurate candidate proposals. Finally, the dual-scale semantic
aggregation module retrieves detailed features at different resolutions to
contribute with the prediction process. Experimental results show that our
method consistently exceeds the few-shot detectors with an excellent
performance gap on all metrics. Moreover, our method achieves the smallest
model size of 19MB as well as being competitive at 50 FPS detection speed
compared with general object detectors. The source code is available at
this https URL

本文提出了一种轻量级有效的少样本探测器来实现在含有黄金图片样本时与一般目标探测相竞争的性能，其中支持特征挖掘块刻画了支持特征中的位置信息的重要性，关系指导块充分利用支持特征引导准确候选区提取，双尺度语义聚合模块检索不同分辨率的详细特征以对预测过程做出贡献。该方法在所有指标上均持续超过现有的少样本探测器，并在模型大小上达到 19MB 的最小值，以及与普通目标探测器相竞争的 50 FPS 探测速度。