1. What is the full name of GPT?
The full name of GPT is Generative Pre-trained Transformer.
2. Who developed GPT?
GPT was developed by researchers at OpenAI.
3. What is the purpose of GPT?
The purpose of GPT is to obtain language understanding ability through unsupervised pre-training, and then apply it to downstream NLP tasks.
4. What type of model is GPT?
GPT belongs to the transformer model family and is a sequence-to-sequence learning model based on the attention mechanism.
5. What is the training data used by GPT?
GPT uses massive Internet data for pre-training, including Wikipedia, news texts, best-selling books on the Internet, etc.
6. What are the components of the architecture of GPT?
The architecture of GPT is mainly composed of word embedding layer, position encoding layer, multi-layer attention and feed-forward neural network.
7. What is the pre-training method of GPT?
GPT uses unsupervised language modeling as a pre-training task, the purpose is to learn the representation and generation of text.
8. What are the applications of GPT?
GPT can be applied to downstream tasks such as machine translation, article generation, question answering, and text summarization.
9. What is the improvement of GPT-3?
Compared with GPT-2, GPT-3 adds more parameters, larger training set, longer context window, etc.
10. How big is the parameter volume of GPT-3?
The parameter volume of GPT-3 has reached 17.5 billion, more than 100 times that of GPT-2.
11. How big is the training data of GPT-3?
GPT-3 uses 570GB of Internet data for pre-training, which greatly increases the amount of data.
12. What is the impact of GPT on artificial intelligence?
GPT proposes an unsupervised pre-training + fine-tuning scheme, which has achieved state-of-the-art effects on many downstream tasks and has had a huge impact on artificial intelligence.
13. What are the shortcomings of GPT?
GPT lacks semantic understanding, and the generated text is incoherent and uninterpretable.
14. How to evaluate the performance of GPT?
The performance of GPT can be evaluated by the difficulty of language modeling, the performance of downstream tasks, and human evaluation.
15. Can GPT understand semantics?
It is difficult for GPT to truly understand semantics, and it relies more on statistical information and context.
16. Can GPT reason?
GPT has a certain reasoning ability, but due to the lack of semantic understanding, its reasoning process is opaque.
17. What is the prospect of GPT?
In the future, GPT can be pre-trained on larger data sets and computing resources, and the language understanding and generation capabilities still need to be improved.
18. What are the limitations of GPT?
The main limitations of GPT lie in the amount of data and computing resources, as well as the lack of semantic understanding of the model.
19. Is GPT suitable for generating long text?
Since the generation process of GPT is automatic regression, the quality of long text generation is poor.
20. What is the innovation of GPT?
The main innovation of GPT is that it proposes a large-scale unsupervised pre-training language model solution, which improves the effect of multiple downstream tasks of NLP.