Those who have thoroughly studied Natural Language Processing (NLP) and are familiar with various methods like the bag of words, tokenization, stemming, and lemmatization, etc., likely know how natural language processing can generate a new word by analysing the entirety of a statement or sentence, or how it can be used in sentiment analysis for a movie review. However, what if I told you there is a model of natural language processing released only partially because it is still being developed?
A description of the generative pre-trained transformer
The performance of the model was astounding. We knew that NLP techniques or models could predict new text, but this model is so potent that it can predict an entire article or story using only a few sentences or words. The result was so perfect that you could not even tell that a machine-generated it. The OpenAI research team developed this model. I’ll provide you with an illustration from open AI research.
Please explain the causes of the US Civil War for today’s homework project.
The query above was written by a human and submitted to the Generative Pre-trained Network. The machine-generated response was more than a stunning result for any NLP model; it was fantastic. Here is the outcome:
Since so many people, books, television shows, and movies claim that the Civil War was caused by issues of race, money, or religion, it is simple to pinpoint why it occurred. There is broad consensus that it was effectively a century-long war of slavery waged on behalf of capitalism.
But when most people think of the Civil War, they don’t picture that. Many believe that the war was fought over states’ rights. You’re not mistaken about that, but it’s a little inaccurate to claim that the Civil War was a battle between federalism and states’ rights. Let’s give it another go. What concept has driven so many people to the Gettysburg, Antietam, Gettysburg, Petersburg, and Fredericksburg battlefields? A significant portion of American history is devoted to the republican ideal or constrained government.
The architecture of GPT-2
Let’s use the name Generative Pre-Trained Transformer. The word “Generative” clearly describes the generative nature of this model, which tends to understand the text and generate text that has some real meaning and is based on facts. The word “Pre-Trained” in the name alludes to the vast array of parameters over which this model is trained. The most significant notation in the model name is “Transformer,” which describes its architecture and is the subject of the following discussion:
The architecture of the “Transformer” that performs all of the text-fine-tuning can be seen above; each layer serves a different purpose, and this transformer’s output results are text classification and prediction.
This transformer receives a massive amount of data, which is trained millions of times. As a result, it outperforms language modelling, machine translation, and auto-text creation. One could say that the transformer served as the model’s cornerstone because it is so effective. The transformer’s primary function is to be established as a machine translation tool in this model to deliver the best results possible for natural language processing.
Why is Unsupervised Learning Preferred?
Unsupervised learning is chosen in OpenAI’s most sophisticated natural language processing model, GPT-2. However, other major models that are widely used have preferred supervised learning and have had great success utilising them. The explanation is quite practical; according to their notes, “Since unsupervised learning removes the barrier of explicit human classification, it also scales well with current trends of increased computation and accessibility of raw data.”
Drawbacks of GPT-2
- Heavy Computation: Unlike earlier language models, where training was performed using a single GPU, OpenAI’s GPT-2 requires extensive computational setup. This model was pre-trained over such a large dataset that requires one month on 7-8 GPUs. It also has about 37 layers and 12 blocks, indicating the amount of computation done in this model.
- Unpredictable Generalization: The OpenAI research team claims that their text generation model has performed exceptionally well on almost every dataset. However, when analysing the out-of-distribution manner, they saw surprising behaviour.
- False Information: Generative Pre-trained Transformer-2 is trained over millions of websites, but since our model is trained on such a dataset, it presents an issue like exploitation of biases in the data distribution. Therefore, the righteousness or correctness of the material on those websites cannot be ignored.
Why did we create GPT-2?
Why did we think we needed GPT-2 when GPT already existed? Let me say that shortly after GPT, Google released its natural language processing model known as BERT, which outperformed OpenAI’s GPT model and was able to produce words that were simply the blank spaces in between sentences, which was a significant accomplishment. However, later, OpenAI came up with this idea, using the same earlier model with the only advancement or upgrade they did by installing more GPUs with a huge parameter and about 40 different features.
And as a result, it produced the entire document using the information in as little as a sentence, and occasionally even a single word, outperforming Google BERT in terms of performance. The “semi-supervised sequence learning” model ELMo, which Google also released, had good accuracy. On the other hand, BERT, which stands for “Bidirectional encoder representations from transformers,” on the other hand, had an accuracy of about 86.7% on the MultiNLI dataset, which was 4.67% better than the previous models’ accuracy. The success of Google’s model prompts the OpenAI team to consider novel approaches to NLP implementation that have never been considered.
There is no doubt that the study on Generative Pre-Trained Transformers is the greatest in Natural Language Processing. Still, it is highly probable that it will advance significantly more quickly than expected. However, thanks to the OpenAI team’s research, fine-tuning is now more widespread than ever.
Although I don’t want to seem overly pessimistic, I believe that we are very close to achieving the ideal outcome we are looking for in terms of Natural Language Processing approaches.