Discover which deep learning architectures are commonly used as pretrained language models. Learn about GPT, BERT, RNN, and LSTM and their applications in NLP.
Table of Contents
Question
Which of the following is NOT a common pretrained language model? (Select all the option that applies)
A. GPT
B. BERT
C. RNN
D. LSTM
Answer
C. RNN
D. LSTM
Explanation
While RNNs and LSTMs are both types of neural network architectures that can be used for natural language processing tasks, they are not themselves pretrained language models.
The key difference is:
- GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers) are specific pretrained language models that have already been trained on large text corpora. They can be fine-tuned for downstream NLP tasks.
- In contrast, RNN and LSTM refer to the underlying neural network architectures. An RNN or LSTM would need to be trained from scratch to create a language model. While an RNN or LSTM could be used to build a pretrained language model, the architectures themselves are not pretrained models.
So in summary, GPT and BERT are examples of common pretrained language models that are widely used, while RNN and LSTM are neural network architectures that language models can be built with, but are not themselves pretrained models.
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