Understanding Encoder and Decoder LLMs (magazine.sebastianraschka.com)

Recent discussions have clarified the distinctions between encoder and decoder styles in large language models (LLMs), which are pivotal in the AI/ML landscape. Encoder-based models, like BERT, excel in generating embeddings for tasks such as classification, learning context through techniques like masked language modeling. In contrast, decoder models, typified by the GPT series, are adept at generating coherent texts through autoregressive techniques, predicting one token at a time based on prior outputs. This fundamental difference dictates their utility; encoders are preferred for understanding and predicting inputs, while decoders shine in creative and generative tasks. The significance of this exploration lies in understanding the architectures that underpin modern natural language processing (NLP) systems. While encoder-only models have become less trendy compared to their generative counterparts, their capacity for producing contextual embeddings remains crucial in many applications. Additionally, hybrid encoder-decoder systems, which combine the strengths of both architectures, are increasingly being leveraged for complex tasks such as translation and summarization. As the AI landscape evolves, clarifying these terms and their implications enhances the ability of the community to develop innovative solutions in NLP and beyond.