跨流派中文歌词作者归属的LLM提示与微调比较研究
A Comparative Study of LLM Prompting and Fine-Tuning for Cross-genre Authorship Attribution on Chinese Lyrics
AI总结：
本文通过对比LLM提示与微调方法，研究中文歌词跨流派作者归属问题，提出新的数据集和领域模型，并探讨流派对性能的影响。
摘要：
我们提出了一项关于中文歌词作者归属的新研究，这是一个缺乏干净、公开数据集的领域。我们的贡献有两个方面：（1）我们创建了一个涵盖多个流派的新的平衡中文歌词数据集；（2）我们开发并微调了一个领域特定模型，将其性能与使用DeepSeek LLM的零样本推理进行比较。我们测试了两个核心假设。首先，我们假设微调模型将优于零样本LLM基线。其次，我们假设性能取决于流派。我们的实验强烈支持假设2：结构化流派（如民间传统）比更抽象的流派（如爱情与浪漫）产生显著更高的归属准确性。假设1只获得部分支持：微调在Test1（真实世界数据和困难流派）中提高了鲁棒性和泛化能力，但在Test2（较小的合成增强集）中提供有限或模糊的收益。我们显示Test2的设计限制（例如标签不平衡、浅层词汇差异和狭窄的流派采样）可能会掩盖微调的真正有效性。我们的工作建立了跨流派中文歌词归属的第一个基准，突显了流派敏感评估的重要性，并为未来研究提供了公开数据集和分析框架。我们最后提出了建议：扩大并多样化测试集，减少对token级数据增强的依赖，平衡各流派作者的表示，并研究领域自适应预训练作为提高归属性能的途径。
英文摘要：
We propose a novel study on authorship attribution for Chinese lyrics, a domain where clean, public datasets are sorely lacking. Our contributions are twofold: (1) we create a new, balanced dataset of Chinese lyrics spanning multiple genres, and (2) we develop and fine-tune a domain-specific model, comparing its performance against zero-shot inference using the DeepSeek LLM. We test two central hypotheses. First, we hypothesize that a fine-tuned model will outperform a zero-shot LLM baseline. Second, we hypothesize that performance is genre-dependent. Our experiments strongly confirm Hypothesis 2: structured genres (e.g. Folklore & Tradition) yield significantly higher attribution accuracy than more abstract genres (e.g. Love & Romance). Hypothesis 1 receives only partial support: fine-tuning improves robustness and generalization in Test1 (real-world data and difficult genres), but offers limited or ambiguous gains in Test2, a smaller, synthetically-augmented set. We show that the design limitations of Test2 (e.g., label imbalance, shallow lexical differences, and narrow genre sampling) can obscure the true effectiveness of fine-tuning. Our work establishes the first benchmark for cross-genre Chinese lyric attribution, highlights the importance of genre-sensitive evaluation, and provides a public dataset and analytical framework for future research. We conclude with recommendations: enlarge and diversify test sets, reduce reliance on token-level data augmentation, balance author representation across genres, and investigate domain-adaptive pretraining as a pathway for improved attribution performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
