用大模型解读文本风格嵌入，让作者归属分析更透明
Latent Space Interpretation for Stylistic Analysis and Explainable Authorship Attribution
- 从隐空间找代表点，用大模型生成风格描述
- 人工评估显示风格描述准确，解释力强
- 辅助人类判断，准确率平均提升20%
当前最先进的作者归属方法在非可解释的隐空间中学习文本的作者特征表示，限制了其在实际场景中的应用。本文提出一种新方法，通过识别隐空间中的代表性点，并利用大语言模型生成每个点对应的写作风格的自然语言描述，实现对隐表示的可解释性。我们评估了可解释空间与原始隐空间的一致性，结果表明其预测一致性优于其他基线方法。此外，通过人工评估验证了风格描述的质量，证明其作为隐空间解释的有效性。最后，我们研究了该系统解释是否能提升人类在复杂作者归属任务中的表现，发现准确率平均提升了约20%。
原文摘要 · Abstract (English)
Recent state-of-the-art authorship attribution methods learn authorship representations of texts in a latent, non-interpretable space, hindering their usability in real-world applications. Our work proposes a novel approach to interpreting these learned embeddings by identifying representative points in the latent space and utilizing LLMs to generate informative natural language descriptions of the writing style of each point. We evaluate the alignment of our interpretable space with the latent one and find that it achieves the best prediction agreement compared to other baselines. Additionally, we conduct a human evaluation to assess the quality of these style descriptions, validating their utility as explanations for the latent space. Finally, we investigate whether human performance on the challenging AA task improves when aided by our system's explanations, finding an average improvement of around +20% in accuracy.
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