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

Large Language Models (LLMs) have demonstrated impressive performances in
complex text generation tasks. However, the contribution of the input prompt to
the generated content still remains obscure to humans, underscoring the
necessity of elucidating and explaining the causality between input and output
pairs. Existing works for providing prompt-specific explanation often confine
model output to be classification or next-word prediction. Few initial attempts
aiming to explain the entire language generation often treat input prompt texts
independently, ignoring their combinatorial effects on the follow-up
generation. In this study, we introduce a counterfactual explanation framework
based on joint prompt attribution, XPrompt, which aims to explain how a few
prompt texts collaboratively influences the LLM's complete generation.
Particularly, we formulate the task of prompt attribution for generation
interpretation as a combinatorial optimization problem, and introduce a
probabilistic algorithm to search for the casual input combination in the
discrete space. We define and utilize multiple metrics to evaluate the produced
explanations, demonstrating both faithfulness and efficiency of our framework.

该研究引入了基于联合提示归属的反事实解释框架 XPrompt，旨在解释少数提示文本是如何共同影响大型语言模型（LLM）的完整生成，并通过定义和利用多个评估指标来展示我们框架的忠实度和效率。

XPrompt：通过联合提示归因解释大型语言模型的生成

XPrompt:Explaining Large Language Model's Generation via Joint Prompt  Attribution

Large Language Models (LLMs) exhibit remarkable generative capabilities,
enabling the generation of valuable information. Despite these advancements,
previous research found that LLMs sometimes struggle with adhering to specific
constraints (e.g., in specific place or at specific time), at times even
overlooking them, which leads to responses that are either too generic or not
fully satisfactory. Existing approaches attempted to address this issue by
decomposing or rewriting input instructions, yet they fall short in adequately
emphasizing specific constraints and in unlocking the underlying knowledge
(e.g., programming within the context of software development). In response,
this paper proposes a simple yet effective method named Chain-of-Specificity
(CoS). Specifically, CoS iteratively emphasizes the specific constraints in the
input instructions, unlocks knowledge within LLMs, and refines responses.
Experiments conducted on publicly available and self-build complex datasets
demonstrate that CoS outperforms existing methods in enhancing generated
content especially for the specificity. Besides, as the number of specific
constraints increase, other baselines falter, while CoS still performs well.
Moreover, we show that distilling responses generated by CoS effectively
enhances the ability of smaller models to follow the constrained instructions.
Resources of this paper will be released for further research.

本文提出了一种名为 “特定性链” 的简单而有效的方法，通过迭代地强调输入指令中的特定约束条件来解锁 LLM 内部的知识，并改进生成的回复内容，实验证明该方法在增强生成内容方面表现优于现有方法，特别是在特定性方面。