A Comparative Study of Transformers and Traditional Deep Learning Models in Natural Language Processing
Abstract
Transformer-based models such as BERT and GPT have revolutionized natural language processing (NLP), but how do they compare to traditional deep learning architectures like RNNs and CNNs? This paper presents a comparative study evaluating the performance of transformers against LSTMs, GRUs, and CNN-based text classifiers. We analyze accuracy, computational cost, and scalability across tasks such as sentiment analysis, machine translation, and question-answering. The results highlight the trade-offs between model complexity, interpretability, and efficiency in modern NLP applications.
References
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