Version | Key Features | Applications |
---|---|---|
GPT-1 (2018) | 117 million parameters, contextual understanding | Basic text generation |
GPT-2 (2019) | 1.5 billion parameters, improved coherence | More advanced text generation, initial chatbot applications |
GPT-3 (2020) | 175 billion parameters, few-shot learning | Content creation, code generation, chatbots |
GPT-4 (2023) | Enhanced accuracy, multimodal capabilities | Advanced chatbots, content analysis, image understanding |
Aspect | GPT | BERT | T5 |
---|---|---|---|
Architecture | Autoregressive, unidirectional | Bidirectional encoder | Encoder-decoder |
Training Objective | Next token prediction | Masked language modeling | Text-to-text transfer |
Directionality | Left-to-right | Bidirectional | Bidirectional |
Primary Use Cases | Text generation, chatbots, content creation | Text understanding, sentiment analysis, question answering | Multitask learning, translation, summarization |
Strengths | Excellent at generating human-like text | Strong contextual understanding | Versatile across various NLP tasks |
Limitations | May generate false information | Less effective for text generation | Requires task-specific fine-tuning |
Notable Versions | GPT-3, GPT-4, GPT-4 Turbo | BERT, RoBERTa, ALBERT | T5, mT5 |