Benchmarking AI Performance: A Comparative Study of Transformer Models and Traditional NLP Algorithms
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Abstract
The rapid advancement of natural language processing (NLP) has introduced transformer-based models like BERT and GPT, which outperform traditional NLP techniques. This paper conducts a comparative study of transformer-based AI models against classical NLP algorithms, such as TF-IDF and LSTMs, on various text classification and sentiment analysis tasks. The study examines performance in terms of accuracy, training efficiency, and computational cost, providing key insights into when and where to deploy modern transformer architectures.
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