Limitations of GPT models - What GPT can't do?

ChatGPT is everywhere. People use it for getting their answers in daily life, search for how-to tutorials and even professionals use it  for coding purposes. The web based browser version alone generated 1.7 billion visits in November 2023.

Infact, ChatGPT is the Gen AI model that changed how conversational AI operates. 

No matter how bad or how good it is. Generative AI development is still baby boomers and is going to digest a lot of implementations as well. We need to sit and watch how it's going to be. 

In the meantime, we will see the upside down of the GPT model, their limitations, What GPT can’t do and the real world scenarios people are experiencing.
 

1. Reliance on Prompts
 

GPT models may give the outputs or generate responses based on the immediate context, potentially leading to surface-level, contextually limited outputs. Deep understanding about the topic is not what they are trained by. 

The effectiveness of GPT is heavily influenced by how prompts are framed, making it crucial for users to carefully structure inputs to obtain desired outcomes. 

Most of the users generally give a piece of one line content to generate the output in terms of content creation. But when doing this, people get the surface level information available on the internet. 
 

2. Repetitiveness
 

GPT's expansive pre-training data mostly result in responses that lack logical coherence. This challenge becomes apparent when the model generates text that deviates from the intended context.

GPT models, in certain scenarios, may generate repetitive content. This repetition can compromise the diversity and quality of the generated text.

This happens when people use generic prompts and do not have the idea of what to acquire through GPT. 
 

3. Hallucination
 

Hallucination, in the mind of GPT models, refers to the generation of content that may stray contrast to the input prompt. This is because GPT wants to generate output to showcase the creativity in it. 

It adds other extra fantasy words or nuances that deviate from factual accuracy.

It may unintentionally introduce creative elements that lack accuracy that results in hallucination
 

4. Vulnerability to Manipulation
 

It describes how the GPT model responds to outside factors that could distort the output that is produced. This may show up as skewed answers or results that follow instructions exactly.

Because GPT models are trained on data, they may unintentionally provide outputs that coincide with leading or biased prompts. This shows a manipulation vulnerability that could damage the objectivity of generated information.
 

5. Lack of Common Sense and Understanding
 

First of all, GPT is a trained model, not a human being. So obviously, it doesn't have the ability to react in the way humans think. They only know contextual things and apply practical reasoning, leading to outputs that may lack intuitive or commonsensical understanding.

Pre-training data may cause GPT models to take cues too literally, which could lead to outputs that aren't sensible as what one would anticipate from human-like language models.
 

6. Difficulty in Handling Specific Tasks
 

GPT models will surely struggle when faced with tasks requiring highly specialized knowledge. The domain-specific details are only trained not they are experienced in. 

The challenge lies in their generalized approach, which might not align seamlessly with the demands of specific, niche tasks.
 

7. Bias and Discrimination
 

GPT models can be tricked. Due to their influence from the training data, GPT models could stay biased in the one side. GPT will show up products that use terminology or specifications used to train the model.

There also can be situations in which the model could generate outputs that reflect racial, gender, or other biases. Based on the training data, GPT shows the biased and discrimination outputs if the prompts are given tricky.
 

8. Lack of Factual Accuracy
 

Sometimes, especially when presented with queries demanding accurate and current factual information, GPT's results can contain errors or misleading.

Although GPT models are good at producing information that is rich in context, they could struggle with factual accuracy. The large amount of pre-training data can occasionally produce wrong outcomes. 

But Google’s Bard can stay ahead in this game since their AI model is updated with sources across the web.
 

What GPT can’t do
 

It can’t think
 

GPT can't think. GPT can mix words together in a polished way but not match the user point of view. It can't analyze, infer, or conclude like a human mind.

Logical reasoning, problem-solving, and concluding things are not done by GPT. 
 

No Originality
 

GPT may generate combinations of existing data, but true originality and innovative ideas are out of its capability. It can't invent brand new concepts or break from the patterns it's been trained on.

The quality GPT's output can vary wildly depending on the prompt and context. You can’t expect consistent output throughout the cycle.

If you clear the history and make a prompt, they generate different results. It will predefine from what you searched in the previous prompt.
 

Summing up
 

As a user you may be frustrated  with the output of the GPT model sometimes but most happy all time. When users are equipped with prompt training, they can get the answers they desire. 

But people don’t have the time in studying and decoding the engineering prompts as many pros will do. Users will go generic in getting their things done or they might need a template to get done. 

If you are an Entrepreneur wishing to start something like AI based tool development, note down these points and come up with a solution.

Because, AI based applications are making people's life easier and widely adopted worldwide. 

Need consultation regarding AI development or GPT integration? Get in touch with us