一种在作者归属分析中利用大语言模型（LLM）能力的贝叶斯方法
A Bayesian Approach to Harnessing the Power of LLMs in Authorship Attribution
AI总结：
本研究针对传统作者归属方法的局限，提出基于贝叶斯方法利用预训练大语言模型概率输出的单样本作者归属方案，在多数据集上取得85%的准确率，设定了新基准并拓展了大语言模型在司法语言学的应用。
摘要：
作者归属分析旨在识别文档的来源或作者。传统方法高度依赖人工特征，且无法捕捉长程关联，限制了其有效性。近期的研究进展利用预训练语言模型生成的文本嵌入，但这类方法需要在标注数据上进行大量微调，存在数据依赖强、可解释性有限的问题。大语言模型（Large Language Models, LLM）凭借其深度推理能力和维持文本长程关联的能力，提供了一种有前景的替代方案。本研究探索了预训练LLM在单样本作者归属分析中的潜力，具体采用贝叶斯方法并利用LLM的概率输出。我们的方法计算一段文本蕴含某作者过往作品的概率，体现了对作者归属更细致的理解。仅使用Llama-3-70B等预训练模型，我们在IMDb和博客数据集上的结果显示，在涉及10位作者的单样本作者分类任务中，准确率达到了可观的85%。我们的研究结果为使用LLM的单样本作者分析设定了新基准，并拓展了这类模型在司法语言学中的应用范围。本研究还开展了大量消融实验以验证我们的方法。
英文摘要：
Authorship attribution aims to identify the origin or author of a document. Traditional approaches have heavily relied on manual features and fail to capture long-range correlations, limiting their effectiveness. Recent advancements leverage text embeddings from pre-trained language models, which require significant fine-tuning on labeled data, posing challenges in data dependency and limited interpretability. Large Language Models (LLMs), with their deep reasoning capabilities and ability to maintain long-range textual associations, offer a promising alternative. This study explores the potential of pre-trained LLMs in one-shot authorship attribution, specifically utilizing Bayesian approaches and probability outputs of LLMs. Our methodology calculates the probability that a text entails previous writings of an author, reflecting a more nuanced understanding of authorship. By utilizing only pre-trained models such as Llama-3-70B, our results on the IMDb and blog datasets show an impressive 85\% accuracy in one-shot authorship classification across ten authors. Our findings set new baselines for one-shot authorship analysis using LLMs and expand the application scope of these models in forensic linguistics. This work also includes extensive ablation studies to validate our approach.
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。
