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Automating news classification with large language models: Exploring fine-tuning, dataset size, and architecture
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Resumo(s)
A comprehensive evaluation benchmarks Large Language Models with traditional machine
learning algorithms for automatic news classification is done on three standard news
classification datasets: BBC News, 20 Newsgroups, and AG News. We implement traditional
models, including Naive Bayes, Logistic Regression, Support Vector Machine, and Random
Forest, to provide clear and interpretable baselines using manual term-frequency and
syntactic features. Then, fine‐tuned transformer architectures, including BERT, RoBERTa, T5,
GPT, and their distilled variants, were used to quantify improvements in predictive accuracy,
resource efficiency, and explainability. Performance is measured via 5-fold cross-validation
using F1 and accuracy metrics, and statistical significance is assessed with a Friedman test
followed by Holm’s correction. Results show that transformer models consistently outperform
classical approaches, with BERT achieving the highest scores under both balanced and
imbalanced conditions. Distilled models rival or surpass full-size transformers on larger
datasets while reducing memory requirements and maintaining comparable inference
latency. Attention‐based attribution methods provide semantic explanations on par with
feature‐importance metrics, confirming that LLMs deliver superior accuracy, adaptability, and
transparency in news classification. Future work should investigate multilingual pretraining,
multilabel classification, and ensemble techniques to further strengthen real‐time,
explainable news‐analysis pipelines.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
Palavras-chave
Large Language Models Text Classification News Classification Fine-Tuning Hyperparameter Optimization BERT SDG 4 - Quality education SDG 9 - Industry, innovation and infrastructure SDG 16 - Peace, justice and strong institutions