寻找内在音乐：探究大语言模型对文学风格的理解
Looking for the Inner Music: Probing LLMs' Understanding of Literary Style
AI总结：
本文通过复现作者识别并扩展至体裁分类，结合句法消融与模型内部探测，揭示大语言模型区分作者与体裁风格的不同机制及关键语言特征。
摘要：
近期研究表明，语言模型经过训练后能够识别远短于传统文体计量学所认为可行长度的文学片段的作者。我们复现了这些作者识别结果，并将其扩展到一个衡量小说体裁的新数据集。我们发现，大语言模型能够区分作者和体裁，但它们的区分方式不同。一些模型似乎更依赖记忆，而另一些模型则更多地从训练中受益以学习作者/体裁特征。随后，我们使用三种方法来探究一个高性能大语言模型用于定义风格的特征。这些方法包括对输入文本进行直接的句法消融，以及两种考察模型内部表示的方法。我们发现，作者级风格比体裁级风格更容易定义，并且更受细微句法决策和语境化词语用法的影响。然而，代词使用和词序等某些特征对定义这两种文学风格都具有重要意义。
英文摘要：
Recent work has demonstrated that language models can be trained to identify the author of much shorter literary passages than has been thought feasible for traditional stylometry. We replicate these results for authorship and extend them to a new dataset measuring novel genre. We find that LLMs are able to distinguish authorship and genre, but they do so in different ways. Some models seem to rely more on memorization, while others benefit more from training to learn author/genre characteristics. We then use three methods to probe one high-performing LLM for features that define style. These include direct syntactic ablations to input text as well as two methods that look at model internals. We find that authorial style is easier to define than genre-level style and is more impacted by minor syntactic decisions and contextual word usage. However, some traits like pronoun usage and word order prove significant for defining both kinds of literary style.
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。
