Is GPT-3 overrated?



OpenAI’s GPT-3 could be the ultimate AI model going forward.

However, it has some limitations which must be overcome by continued research and development. The limitations of GPT-3 must be recognized and analyzed to know the areas in which it can improve.

The Generative Pre-trained Transformer 3 (GPT-3) is a formidable creation that promises to take the field of artificial intelligence forward in leaps and bounds. The powerful text generator provides insight into what natural language processing models and neural networks of the future can do. GPT-3 is the most advanced and powerful language model ever created. It has an incredible 175 billion parameters to regulate its language processing operations. Other stats and specs associated with GPT-3 highlight its incredible powers and abilities. While GPT-3 will continue to receive rightly praise for what it can accomplish, it’s easy to get carried away, and gradually the appreciation turns into hyperbole. The GPT-3 formed by deep learning, created by OpenAI, has certain limitations that must be overcome in order for it to become even better. Here are some of the identified limitations of the GPT-3 models.

GPT-3 cannot “understand” semantics

At a basic level, GPT-3 can be classified as an advanced predictor and text generator. To demonstrate the basic capability of GPT-3, all a data scientist needs to do is provide the model with a few lines of text to work on. The model predicts which text will follow this block of data by eliminating all incorrect alternatives. After creating a larger body of text, the model continues to generate more data using the initial input and the first output together as the second block of input text. This text generation process continues until a large article or essay is created. Text length and basic edits should be done by data scientists and language experts along the way. In 2020, an article published on The Guardian’s website was written by GPT-3 and edited by the media company’s editors later, thus following the above process.


The basic functioning of GPT-3 depends on its ability to scan the entire web in nanoseconds for references and patterns related to a given topic (and other topics related to it). After going through billions of web pages containing text previously posted to the internet, the model generates words that are mathematically and logically plausible responses to the input. Now, while all of this information on board almost certainly ensures that GPT-3 detects incredibly deep and nuanced inferences from massive input data sets, there are some comprehension issues that can creep into the output text. GPT-3 does not really “know” or “understand” what certain words mean in specific situations. The model does not have an internal component that helps to understand the semantic realities of the world. As a result, while some of the text generated by GPT-3 may make mathematical sense, it may be incorrect, racist, sexist, or biased because there are no filters in the template. text generation.

In other words, GPT-3 lacks the ability to reason beyond what is statistically sound text. Additionally, if the model encounters content that may not be adequately represented on the internet, it cannot generate meaningful and consistent output text. There are several examples of limitations of GPT-3 on the Internet. GPT-3’s semantic limitations make it slightly unreliable for compiling and generating rare and complicated text topics.

GPT-3 cannot create consistent long articles

Tellingly, The Guardian published a follow-up article to the first. The second article contained a step-by-step guide to creating cohesive prose based on the output pieces of GPT-3. The first step states that organizations or individuals using GPT-3 to create articles should first seek the services of a computer scientist to make the text generation model work in the first place. This means that organizations that install GPT-3 for their daily work will have to shell out more money to use it properly. More importantly, however, there are several steps organizations need to take to get useful output from their GPT-3 compatible systems, including “erasing the flow of robotic consciousness that appeared in the worst GPT-3 text output.”

While you can see that there are several costs associated with optimizing the output of GPT-3, the biggest problem is that the powerful text generator cannot be used to compose large articles or blogs. Basically, GPT-3 generates its output in a word-by-word format. Its output depends on several parameters, including the text immediately surrounding the primary input data. As a result, long articles do not contain a cohesive narrative after more than a few paragraphs. We humans have natural cognitive and comprehension abilities that allow us to hold a point of view in long pieces of prose, even over thousands of words. GPT-3, on the other hand, will generate an article that goes completely off topic or contradicts itself by the second or third paragraph. As a result, the outputs of GPT-3 lack a sense of purpose and this limits their usability in content creation.

More worryingly, creating any type of article with the participation of GPT-3 is a cumbersome and impractical exercise for organizations. In this study, data experts identified the difficulties in the process of generating text using the output of GPT-3. To create an article, editors and AI experts must work together to ardently compile 500-word essay pieces from the massive stream of rambling and often meaningless output texts provided by a GPT-powered NLP system. 3. As a result, to write an article of, say, 5,000 words, experts would spend many hours creating 10 (!) Carefully selected 500-word chunks before digitally assembling them into a somewhat sane article. It is found that only about 12% of the content generated by GPT-3 can be used in articles. Thus, the remaining 88% are provided by human beings. As a result, using GPT-3 for content creation is expensive, difficult, and ultimately inefficient because a large percentage of its output is unfit for use.

GPT-3 output content may be objectionable

While creating inconsistent text is a waste for organizations, it is better than generating content that is categorically offensive and, therefore, downright unacceptable for publication. Unfortunately, GPT-3 can also create this type of content. Some studies have noted GPT-3’s penchant for generating toxic language that can demonstrate biases based on race, religion, gender, ethnicity, among other aspects. Although this limitation of GPT-3 stems mainly from the narrowness of the data sets used to train neural networks, it renders the model unusable for sensitive operations. As we know, the internet is full of toxic and obnoxious web pages that contain inflammatory articles and speeches. If GPT-3 is allowed to digest such content on the Internet, then its output will simply reflect negativity. The output of a system powered by GPT-3 can be racist.

Racial prejudice is also quite common in the text generated by GPT-3. OpenAI themselves admit that their API models demonstrate discriminatory tendencies which will be exposed in the text generated by GPT-3. This particular limitation of GPT-3 can be corrected by creating clear guidelines and rules indicating which web pages the template is allowed to use for text generation SEO and which areas of the internet are off limits for this purpose.

GPT-3 may not even be classified as AGI

GPT-3’s lack of semantic know-how, limited causal reasoning, and generalization of the text have led many to believe that it is nothing more than a glorified transformer. GPT-3’s text generation capabilities are a result of its built-in data recovery resources and extensive pre-training procedures. Text generated by a GPT-3 powered system almost always has to go through multiple editing and monitoring modules before it can even be remotely presentable in order to be published. As we have seen, humans must be heavily involved in overseeing screening processes. Therefore, these limitations of GPT-3 make AI experts believe that text generation models cannot be lumped alongside other General Artificial Intelligence (AGI) models.

-It is unclear whether GPT-3 is overrated or not. The technology has its weaknesses and must overcome several limitations to be classified as a perfect NLP model and a worthy supporter of AI. There are several articles on the Internet that simply take the plunge and state that GPT-3 can be used in areas as diverse as healthcare. While arguments can be made for this, the point is that, at least for now, the use of GPT-3 should be focused on specific fields and tasks that make full use of its powerful prediction and generation capabilities. of text. In addition, models require more precise tuning and several development cycles before they can be used for more important purposes. Perhaps one should imagine that the GPT-3 is a really powerful fighter plane. Despite its amazing capabilities, such a machine cannot be used for space exploration missions due to its obvious limitations in this area. Likewise, we need to keep GPT-3’s limitations in mind before preparing it for failure with unrealistic expectations.


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