LLM单次风格迁移用于作者身份识别与验证
One-shot Style Transfer LLM log-probabilities for Authorship Attribution and Verification
AI总结：
本文提出利用LLM的单次风格迁移能力，通过无监督方法提升作者身份识别与验证的性能，同时优化计算成本与任务表现的平衡。
摘要：
计算风格学研究通过定量文本模式来研究写作风格，使作者身份识别、身份链接和抄袭检测等应用成为可能。现有的监督和对比方法往往依赖于存在伪相关关系的数据集，将风格与主题混淆。尽管语言模型与这些任务相关，但现代大型语言模型（LLMs）的预训练在一般的作者身份分析中被低估了。我们介绍了一个无监督框架，利用LLM的对数概率来测量两段文本之间的风格迁移性。该框架利用了现代LLM的广泛CLM预训练和上下文能力。我们的方法避免了使用具有伪相关数据的显式监督。我们的方法在相似模型规模下显著优于无监督提示基线方法，并在考虑主题重叠时超过对比训练模型。该框架的性能随着模型规模的增加而提高。在作者身份验证的情况下，我们提出了一种额外的机制，通过增加测试时间的计算来提高准确性；使在计算成本和任务性能之间实现灵活权衡成为可能。
英文摘要：
Computational stylometry studies writing style through quantitative textual patterns, enabling applications such as authorship attribution, identity linking, and plagiarism detection. Despite the relevance of language modeling to these tasks, the pre-training of modern large language models (LLMs) has been underutilized in authorship attribution and verification. We introduce an unsupervised framework that uses the log-probabilities of an LLM to measure style transferability between two texts. This framework takes advantage of the extensive Causal Language Modeling (CLM) pre-training, one-shot capabilities and scale of LLMs, avoiding explicit supervision. Our methods substantially outperform prompting-based unsupervised baselines in authorship verification at similar model sizes, and is competitive with or improves contrastive baselines in most settings with sufficient model scale. We further observe strong performance across non-English languages. The effectiveness of the proposed framework improves consistently with increasing model scale. In the case of authorship verification, we propose an additional mechanism that increases test-time computation to improve accuracy; enabling flexible trade-offs between computational cost and task 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。
