If you’re somewhat acquainted with AI paraphrasers, you’ve probably heard and read the word ‘GPT-3’ a lot. You may have seen some online tools bragging about how their algorithms are driven and empowered by GPT-3 or you may have seen some platforms describing how the secret ingredient behind their super-intelligent results is GPT-3.
Fine, but what is GPT-3? And how does it improve AI paraphrasing?
If the answers to these questions are what you’re looking for, then you’re at the right place. In this blog post, we will be looking at GPT-3 and how it helps paraphrasing tools to create smart results. We’ll also show some of the popular paraphrasing tools that use this model and how it affects their output.
What is GPT-3?
To do justice to this question, we would have to write a whole book solely dedicated to it. But, since we lack the capacity for that, we’re just going to stick to a rough introduction and outline of what this model does.
Essentially, GPT-3 is a machine-learning model that can perpetuate a “prompt” (e.g., a small piece of text) and generate content following the same idea, concept, tone, or style.
In layman’s terms, you can give GPT-3 some text and it can generate a whole bunch of content related to it. This is, by the way, the whole gimmick behind AI content generators. These types of tools are given a keyword or a topic, which they later dilate on…extensively.
GPT-3 is essentially the third entry of OpenAI’s GP (Generative Pre-Trained) models. That is what the ‘3’ stands for. As compared to its predecessor i.e., GPT-2, GPT-3 is a lot more advanced, dynamic, and intelligent.
This can be gauged easily from the fact that while GPT-2 was trained for word prediction using 40GB of internet data, GPT-3 was trained using a whopping 570GB in total. GPT-2 had 1.5 billion parameters on its final model whereas GPT-3 had 175 billion parameters on release.
Although we are discussing how GPT-3 is improving paraphrasing in today’s world, there is a lot more that it can do.
Moving on, let’s take a look at how GPT-3 features in paraphrasing tools and how it helps them in generating coherent, human-like results.
How Does GPT-3 Feature in Paraphrasing Tools?
Paraphrasing tools use both: GPT-3 and NLP. NLP stands for Natural Language Processing, and it is basically the technology using which computerized software is able to understand natural languages i.e., the ones spoken by humans.
A paraphrasing tool essentially uses NLP to initially understand the input text, and then it uses GPT-3 for making such changes to the text that resonate with its context and meaning.
For example, when an AI-driven paraphrasing tool synonymizes (i.e., replaces the original words with their synonyms) the input text, it uses GPT-3 to understand the context so that it can only add those words that don’t alter the overall meaning.
The same goes for more extensive changes such as changing sentence structures and word forms etc.
To help you understand this better, let’s move on to look at some popular paraphrasing tools and how they utilize GPT-3 for providing smart results.
How GPT-3 Improves Paraphrasing Tools: The Demo
Paraphrasing Tool by Prepostseo.com
The paraphrasing tool by Prepostseo is AI-based, and it uses GPT-3 to generate intelligent-sounding results. Since we want to talk about how GPT-3 improves and helps paraphrasing tools, we will use complicated and contextually-challenging sentences. This will help us understand just how well the model works to understand the context of the input and make changes accordingly.
Test # 1: Using the ‘Fluency Mode
Here is the text that we are going to be entering into the tool:
The man ran down to the clinic as the blood ran down his forehead. He stopped running just in time to avoid getting run over by the bus.
We used similar-sounding phrases with different contextual meanings to make things a little difficult for the tool.
Here is how the result came out:
Output: The man ran to the clinic as blood ran down his forehead. He stopped running just in time to avoid being hit by the bus.
Now, there is one major aspect that we want to point out here.
In the input text, the word ‘run’ was used in different forms, which each instance of the word having a different meaning. For example, the first ‘ran’ meant that the man ‘sprinted’ down to the hospital. The second ‘ran’ meant that the blood was ‘trickling’ down his forehead. And the third ‘ran’ meant ‘getting hit’ by the bus.
Despite all three words being the same, the paraphrasing tool was able to detect the meaning of each instance. Due to that, it smartly changed the words ‘getting run over’ with the words ‘being hit’.
This is a very small but effective example of how GPT-3 improves paraphrasing tools. Although the input text was challenging (i.e., for a machine), the tool did not lose touch with the meaning since it understood the initial prompt.
Test # 2: Using the ‘Creative’ Mode
The text that we used above was a little short. While it did convey the idea, it wasn’t really extensive.
For the next demo, we will use longer text to show the extent of how well GPT-3 can understand the context and help the tool to make suitable changes.
Here is the text that we will be using this time:
Input Text: Fearing that he would be arrested, the man ran away from the scene. It was later that he realized that no one gets arrested for spilling coffee on the street. And it was much later on that he realized that the “street” was the driveway of his own home.
Output Text: Afraid of being arrested, the man fled. It was later that he realized that no one was arrested for spilling coffee on the street. And it was much later that he realized that the “street” was his driveway.
In this example, there are a couple of things that have to be noted. Firstly, you will see that the input text was significantly larger than the output provided by the tool. Other than making changes to the words and phrases of the text, the tool also made it concise.
Secondly, let’s look at the changes themselves.
To start with, the tool changed the words ‘Fearing that he would be arrested…’ to a simple ‘Afraid of being arrested…’ Similarly, the long phrase ‘…ran away from the scene…’ was changed to merely ‘fled’.
A subtle point to note here is that the word ‘scene’ has more than one meaning. It can be used to denote the sequence in a video or a play etc. But, despite that, the tool recognized its contextual meaning from the preceding words ‘ran away’. That is why it understood that the word ‘fled’ would be best suited to replace the phrase.
Next up, we will look at paraphrasing tool offered by Paraphraser that uses GPT-3 to paraphrase text. We won’t dive into different modes with this one. We’ll just run a test to show the role and effectiveness of GPT-3 in understanding the text and paraphrasing it properly.
Like the text used above, we will try and make this input a little difficult for the tool. This will be a short one, though.
Input Text: He was lying down on the bed but he was lying about going to the store.
Once again, in this input, we’ve used two same words with different meanings to see how well the tool tackles it. The word ‘lying’ in the first clause is synonymous with ‘resting’ whereas the word ‘lying’ in the second clause is synonymous with ‘speaking falsehood’.
Let’s actually digress a little here to explain why we’ve used these types of words here. A normal non-GPT-3 tool would not recognize the difference in the meanings of the two same words i.e., ‘lying’. Such a tool would change both instances of the same word with nonsensical synonyms. But a tool that does use GPT-3 would understand the context and make changes accordingly.
Coming back to the point, here is how the tool paraphrased the input:
Output Text: He lay in bed, but lied about going to the store.
This is actually pretty impressive. In the output, the tool eliminated the confusion of the two identical words. The word ‘lay in bed’ is different from the word ‘lying’ in the input…but it conveys the same meaning.
Before we wrap our discussion up, we’ll look at one more tool that uses GPT-3 for paraphrasing text i.e., Wordtune.
Wordtune generally makes extensive changes to the input text, which is a little different from the other tools we’ve discussed above. That is why we want to discuss this tool before we end the post.
Since Wordtune works on a sentence-by-sentence algorithm, we will be using a single line to see demonstrate the working and effectiveness of GPT-3.
Input Text: The rich man enjoyed his rich coffee while sitting next to the richly colored flowers.
Output Text: With his rich coffee in hand, the rich man sat beside the colorful flowers and enjoyed it.
In these results, we can see how the tool changed the words ‘richly colored’ to simply ‘colorful’. Surprisingly, the tool also added the words ‘in hand’ when providing the output, whereas these words don’t exist in the input at all. This is yet another aspect that shows us the ability of GPT-3 in helping paraphrasers generate smart results.
So, there you have it.
GPT-3 is a revolutionary technology and it can help AI tools in providing excellent results based on an initial input/prompt. In the post above, we looked at three different tools that employ this model and how it helps them in creating intelligent outputs.