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Machine learning models achieve high accuracy in thematic indexing of Voltaire's works

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a machine learning approach for automatic thematic indexing of large literary corpora, using the complete works of Voltaire as a test case. The study frames this task as a multi-label classification problem, comparing various models including encoder-based and generative LLMs fine-tuned with LoRA. The best-performing model, a quantized Mistral variant, achieved F1 scores up to 0.67, demonstrating potential for structured thematic access to historical texts. AI

IMPACT This research demonstrates how LLMs can be applied to complex literary analysis, potentially streamlining scholarly access to historical texts.

RANK_REASON Academic paper detailing a machine learning approach to thematic indexing of literary works.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Machine learning models achieve high accuracy in thematic indexing of Voltaire's works

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Miguel Arana-Catania, Gillian Pink, Glenn Roe ·

    Automatic Thematic Indexing of Large Literary Corpora: A Machine Learning Approach to Voltaire's Complete Works

    arXiv:2607.09316v1 Announce Type: cross Abstract: Thematic indexing -- the practice of assigning structured conceptual labels to sections of text -- is essential to scholarly access in large-scale literary and historical editions, yet it remains a largely manual, labour-intensive…

  2. arXiv cs.AI TIER_1 English(EN) · Glenn Roe ·

    Automatic Thematic Indexing of Large Literary Corpora: A Machine Learning Approach to Voltaire's Complete Works

    Thematic indexing -- the practice of assigning structured conceptual labels to sections of text -- is essential to scholarly access in large-scale literary and historical editions, yet it remains a largely manual, labour-intensive process. This paper explores the application of m…

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