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    <abstract>In the context of text classification, the financial burden of annotation exercises for creating training data is a critical issue. Active learning techniques, particularly those rooted in uncertainty sampling, offer a cost-effective solution by pinpointing the most instructive samples for manual annotation. Similarly, Large Language Models (LLMs) such as GPT-3.5 provide an alternative for automated annotation but come with concerns regarding their reliability. This study introduces a novel methodology that integrates human annotators and LLMs within an Active Learning framework. We conducted evaluations on three public datasets. IMDB for sentiment analysis, a Fake News dataset for authenticity discernment, and a Movie Genres dataset for multi-label classification.The proposed framework integrates human annotation with the output of LLMs, depending on the model uncertainty levels. This strategy achieves an optimal balance between cost efficiency and classification performance. The empirical results show a substantial decrease in the costs associated with data annotation while either maintaining or improving model accuracy.</abstract>
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%0 Conference Proceedings
%T Enhancing Text Classification through LLM-Driven Active Learning and Human Annotation
%A Rouzegar, Hamidreza
%A Makrehchi, Masoud
%Y Henning, Sophie
%Y Stede, Manfred
%S Proceedings of the 18th Linguistic Annotation Workshop (LAW-XVIII)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julians, Malta
%F rouzegar-makrehchi-2024-enhancing
%X In the context of text classification, the financial burden of annotation exercises for creating training data is a critical issue. Active learning techniques, particularly those rooted in uncertainty sampling, offer a cost-effective solution by pinpointing the most instructive samples for manual annotation. Similarly, Large Language Models (LLMs) such as GPT-3.5 provide an alternative for automated annotation but come with concerns regarding their reliability. This study introduces a novel methodology that integrates human annotators and LLMs within an Active Learning framework. We conducted evaluations on three public datasets. IMDB for sentiment analysis, a Fake News dataset for authenticity discernment, and a Movie Genres dataset for multi-label classification.The proposed framework integrates human annotation with the output of LLMs, depending on the model uncertainty levels. This strategy achieves an optimal balance between cost efficiency and classification performance. The empirical results show a substantial decrease in the costs associated with data annotation while either maintaining or improving model accuracy.
%U https://aclanthology.org/2024.law-1.10/
%P 98-111

Markdown (Informal)

[Enhancing Text Classification through LLM-Driven Active Learning and Human Annotation](https://aclanthology.org/2024.law-1.10/) (Rouzegar & Makrehchi, LAW 2024)

• Enhancing Text Classification through LLM-Driven Active Learning and Human Annotation (Rouzegar & Makrehchi, LAW 2024)

ACL

• Hamidreza Rouzegar and Masoud Makrehchi. 2024. Enhancing Text Classification through LLM-Driven Active Learning and Human Annotation. In Proceedings of the 18th Linguistic Annotation Workshop (LAW-XVIII), pages 98–111, St. Julians, Malta. Association for Computational Linguistics.