Comparative Analysis of Encoder-Only, Decoder-Only, and Encoder- Decoder Language Models

: With the surge in Artificial Intelligence (AI) popularity sparked by ChatGPT, a plethora of Transformer-based models have emerged, and the decoder-only architecture has become the mainstream development direction of large language models (LLMs) in most big-tech companies. In the rapidly advancing field of Natural Language Processing (NLP), understanding the capabilities and limitations of different language model architectures is critical for pushing the boundaries of AI. This paper delves into the comparative analysis of encoder-only, decoder-only, and encoder-decoder models, illuminating their strengths, weaknesses, and optimal use cases within the landscape of NLP. Encoder-only models are highlighted for their efficiency and deep understanding, decoder-only models for their generative capabilities and adaptability, and encoder-decoder hybrids for their versatile application across a broad spectrum of NLP tasks. This comparative analysis provides valuable insights into the strategic deployment of these models in real-world applications and underscores the ongoing need for innovation in model architecture to optimize performance and computational efficiency.