Google XLNet

Google XLNet was developed in 2019 by researchers at Google and Carnegie Mellon University. It was created to overcome the limitations of previous language models like BERT by using a permutation-based training method, which allowed it to capture bidirectional context more effectively and improve performance on various natural language processing tasks.

Strengths of Google XLNet include superior performance in natural language understanding tasks, effective bidirectional context capture, and improved text generation capabilities. Weaknesses involve higher computational requirements and complexity compared to simpler models. Competitors include OpenAI's GPT-3, BERT, and T5.

A Google XLNet expert must have strong skills in Python programming, deep learning frameworks like TensorFlow or PyTorch, natural language processing techniques, and familiarity with transformer architectures. Proficiency in handling large datasets and experience with model fine-tuning and optimization are also essential.