Do LLMs Understand Why We Write Diaries? A Method for Purpose Extraction and Clustering
Valeriya Goloviznina, Alexander Sergeev, Mikhail Melnichenko, Evgeny Kotelnikov

TL;DR
This paper presents a novel LLM-based method for extracting and clustering purposes behind diary writing, applied to Soviet-era diaries, revealing insights into authors' intentions and model limitations.
Contribution
Introduces a new LLM-driven approach for purpose extraction and clustering in diary analysis, evaluated on historical diaries with insights into model performance and errors.
Findings
GPT-4o and o1-mini outperform baseline models
Purpose analysis reveals gender, age, and temporal patterns
Models exhibit specific error types highlighting areas for improvement
Abstract
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,…
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Taxonomy
TopicsNatural Language Processing Techniques · Semantic Web and Ontologies · Digital Rights Management and Security
