Please use this identifier to cite or link to this item:
https://repository.hneu.edu.ua/handle/123456789/39401| Title: | Small Language Models for perplexity-based text classification |
| Authors: | Gorokhovatskyi O. |
| Keywords: | Small Language Models (SLMs) Text Classification Perplexity AI-generated Content Gemma 3 Llama 3.2 Convolutional Neural Networks (CNN) Ukrainian Language |
| Issue Date: | 2026 |
| Citation: | Gorokhovatskyi O. Small Language Models for perplexity-based text classification / O. Gorokhovatskyi // Сучасні інформаційні системи та технології в цифровому суспільстві : матеріали Міжнародної науково-практичної конференції тези доповідей, 16 – 17 квітня 2026 р. : тези допов. – Харків : ХНЕУ імені Семена Кузнеця, 2026. – С. 86. |
| Abstract: | The research explores the use of Small Language Models (SLMs), specifically those with 1B parameters or fewer, to classify text as either AI-generated or human-written in the Ukrainian language. By leveraging perplexity – a measure of a model's uncertainty – as a key feature, the study evaluates the performance of models like Gemma 3 and Llama 3.2. The authors further propose a classification method using Convolutional Neural Networks (CNN) trained on token-level probability vectors, achieving accuracy rates up to 0.8559. |
| URI: | https://repository.hneu.edu.ua/handle/123456789/39401 |
| Appears in Collections: | Статті (ІКТ) |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| SMALL LANGUAGE MODELS FOR PERPLEXITYBASED TEXT CLASSIFICATION.pdf | 418,51 kB | Adobe PDF | View/Open |
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