There are various types of large language models. These models vary in architecture and are trained for tasks such as text generation, language understanding, and information retrieval. Some of the popular ones could be these:
BERT (Bidirectional Encoder Representations from Transformers): Developed by Google, it understands language by looking at words from both sides to capture context better.
T5 (Text-To-Text Transfer Transformer): This model sees all language tasks as turning input into output, making it versatile for various language jobs.
XLNet: It combines two approaches to understand language better, making it good at maintaining context.
RoBERTa (Robustly optimized BERT approach): A tweaked version of BERT, it’s designed to perform well on understanding tasks.
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