用大模型解析日记写作目的，发现不同时期作者动机差异
Do LLMs Understand Why We Write Diaries? A Method for Purpose Extraction and Clustering
- 用大模型识别日记背后的写作意图，如记录生活、自我反思等
- GPT-4o和o1-mini在识别日记目的上表现最优，优于模板方法
- 分析显示性别、年龄、年代影响写作动机，模型错误揭示改进方向
日记分析面临从大规模文本中提取有效信息的挑战，传统方法常难以取得理想效果。本文提出一种基于大语言模型（LLMs）的新方法，用于识别并聚类日记写作的不同目的。此处的“目的”指写作意图，如记录生活事件、自我反思或练习语言技能。该方法应用于苏联时期（1922–1929）来自Prozhito数字档案馆的个人日记，这是一个丰富的个人叙事集合。我们评估了多种专有与开源大模型，发现GPT-4o和o1-mini表现最佳，而基于模板的基线方法显著逊色。此外，我们按作者性别、年龄及写作年份分析了提取出的目的分布，并考察了模型产生的错误类型，从而更深入理解其局限性与未来研究的改进空间。
原文摘要 · Abstract (English)
Diary analysis presents challenges, particularly in extracting meaningful information from large corpora, where traditional methods often fail to deliver satisfactory results. This study introduces a novel method based on Large Language Models (LLMs) to identify and cluster the various purposes of diary writing. By "purposes," we refer to the intentions behind diary writing, such as documenting life events, self-reflection, or practicing language skills. Our approach is applied to Soviet-era diaries (1922-1929) from the Prozhito digital archive, a rich collection of personal narratives. We evaluate different proprietary and open-source LLMs, finding that GPT-4o and o1-mini achieve the best performance, while a template-based baseline is significantly less effective. Additionally, we analyze the retrieved purposes based on gender, age of the authors, and the year of writing. Furthermore, we examine the types of errors made by the models, providing a deeper understanding of their limitations and potential areas for improvement in future research.
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