Since the introduction of GPT-3, content creators have increased the number of SEO use cases. A bi-monthly update to review new progress in the field of language models appears to be in order.
To begin with, by the end of 2021, the very large language models club had grown significantly.
Each country has attempted to showcase and make available its technologies through research papers and public or private demonstrations.
The following are the race’s main competitors:
- OpenAI – Turing NLG in the United States.
- Wu Dao 2.0 – PanGu-Alpha in China
- HyperCLOVA, HyperCLOVA, HyperCLOVA, HyperCLOVA, HyperCLO
- A121 in Israel (Jurassic-1).
- Aleph Alpha in Europe.
- EleutherAI is open source.
Each model has advantages and disadvantages.
Many SEO software editors and SEO agencies are now putting these models to the test.
How Do I Select a GPT-3 Model?
You might believe that the more parameters the model has, the better it is (Editor’s note: a parameter corresponds to a concept learned by the AI).
But you’d be mistaken.
The number of parameters is not the most important criterion, because lighter models can produce excellent results.
Rather, it is the data that was used to train the model.
In fact, for a model to be effective, it must be able to comprehend a wide range of disparate domains.
The first step is to determine how the model was trained. The diagram below can help with GPT-3:
As we can see, GPT-3 was primarily trained using data from:
- Between 2016 and 2019, there was a web archive.
- WebText, which corresponds to web-based data retrieval.
- Wikipedia.
- English-language books (Books1)
- Other language books (Books2).
When we examine how the open-source models are trained, we can see that the sources are quite different.
Everything is based on the project The Pile, which is an 825 GB data set of diverse English texts that is free and open to the public.
We can find a wide range of data in The Pile, including books, GitHub repositories, webpages, discussion journals, and articles in medicine, physics, mathematics, computer science, and philosophy.
In general, it will be critical to test the language model in your language, particularly on the vocabulary of your website.
Let’s start with the pitfalls before we get into specific SEO use cases.
Pitfalls of GPT-3 Content Generation for SEO
It is critical to understand the pitfalls to avoid when creating qualitative texts that will pique your users’ interests.
First and foremost, whatever model you choose, you must provide it with high-quality examples as input so that it can imitate them and, most importantly, respect a specific type of text.
If you ask a language model to generate content about “New York plumbers,” it will take you down a number of unsuitable paths:
- Should it make a fictitious directory?
- Should it make content about a plumber in New York?
- Should it start a conversation among Paris plumbers?
- Maybe a poem about New York plumbing?
In a nutshell, the model will be lost.
Second, language models have no way of dealing with duplicate content.
As a result, whatever text you generate will require the use of a third-party tool to ensure that the model has not duplicated something it has learned – and, more importantly, that the text does not already exist and is unique.
There are numerous tools available to confirm the uniqueness of your content. If it isn’t, simply reload the page.
Furthermore, content generation templates do not optimize text for search in any way.
Again, they are trained on a wide range of sources, so you will need to guide them using all of the semantic tools available on the market.
You can also request that they emphasize keywords and explain your concepts in greater depth.
Finally, the model has the ability to generate data. Models, in fact, have a setting for creativity.
If the model is set to allow for a high level of creativity, it may invent characteristics for an object, for example, resulting in inconsistencies in your texts.
Read: 4 Things You Should Always Consider to do Next-level Guest Blogging
SEO Content Generation Use Cases
I’ve been experimenting with various content generation use cases for the past eight months.
We’ve had very good results with free templates like GPT-Neo and GPT-J on the following use cases:
- Concept extraction (extraction of places, people, etc.).
- Question extraction from a text.
- Answers to text-based questions
- Only short texts in English are generated in order to create titles or meta descriptions.
On the other hand, using a paid model like GPT-3 with accessible pricing, we were able to generate not only long and specific texts but also sophisticated textual summaries.
The image is difficult to read, but it shows the input examples in bold, followed by the text generated by the model in italics.
The goal here is to create an incentive text that will entice people to buy a product.
GPT-3 fully comprehends the required tone and length of the text, as well as the words to be used. The text is generated in under ten seconds!
The generated text is as follows:
Allow yourself to be influenced by your childhood fantasies. Do you recall your first video game? You can relive them thanks to technological advancement. You can play with another player while traveling by using the Nintendo DS, PlayStation, or Xbox consoles!
Try out fun games like Mario Kart 8 Deluxe on the Nintendo Switch – race against your opponents and enjoy magical adventures with Mario, Zelda, and other characters. Enjoy an incredible selection of AAA games; whether you prefer fighting games, adventure games, or multiplayer games, PS4 has PS Plus memberships to suit your preferences!
For a small fee, Xbox also provides unlimited access to entertainment apps such as Netflix and YouTube. Whether you choose the PS4, Xbox One, or the new Nintendo Switch, it will be simple to connect with family or friends via applications like Skype for a fun time with your friends.
Don’t let this opportunity pass you by!
Of course, proofreading is still necessary to improve the text, but this technology saves time and allows humans to focus on high-value-added problems.
Let us now look at two content generation revolutions.
Two SEO Revolutions
As we previously saw, GPT-3 may not have been able to generate specific content because it was not trained to do so.
You now have the option of retraining the model.
Simply provide it with between 20 and 100 examples containing your instructions as input and the ideal completion you want it to produce as output:
{"prompt":"CTA:\n\nsubject: smartphone\n\n words: smartphone, Samsung, mobile phones, Xiaomi, Samsung, Apple, Huawei, Sony, iPhone, intelligent mobile phones, Android\n\n ","completion":"Looking for a new smartphone? You've shortlisted a Samsung Galaxy or a Huawei model, but you haven't made a decision yet? Choose a smartphone in our product range and enjoy the best of the new mobile phones at an unbeatable price! You're guaranteed to find a smartphone at the best price, so don't miss out! It's never easy to find the best option among a multitude of smartphones: Xiaomi, Samsung, Apple, Huawei, Sony, or even Honor. Smartphones - intelligent mobile phones - aren't just synonymous with Apple's iPhone, but include many models. Smartphones using the Android operating system are the most common."}The operation is time-consuming, but the end result is an optimized template that fits your use case, especially if you need to use a specific vocabulary when optimizing your SEO for a specific niche, industry, or theme.
It only takes two lines of Python code to accomplish this. Of course, the most time-consuming part is creating this example file.
Finally, let’s get to the topic I was most excited about this month: code generation!
Indeed, new technology has been released in which we provide instructions and the new OpenAI Codex engine generates Python code to solve our problems.
Let us begin by emphasizing that these are simple issues: it cannot replace developers because we would need to provide the AI with all of the code setup as well as all of the technical constraints.
However, from a pedagogical standpoint, and especially in a no-code approach, it is fantastic to be able to ask it to connect to a data source (Mysql, Excel, CSV, API, etc.) and generate the appropriate views in a matter of seconds.
Here’s a mini-example in which I retrieve the NASA log file for August 1, 1995, and request a bar graph displaying the total number of URLs visited in the hour.
Then, by copying and pasting the code into a simple text editor, you can see the result.
To push the no-code concept even further, I’m working on a web application that will be entirely driven by text.
The only limit to using language models in SEO is your own creativity. You can certainly create an entire SEO dashboard in this manner by breaking down each of the desired views steps by step.
Language models still have a lot of surprises in store, and there will be a lot of new uses for marketing in the near future.
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