Know More About ChatGPT-Based ML Models: Advantages And Challenges

The AI-based technology, ChatGPT, is an example of modern-day sophisticated machine learning (ML) models. A comprehensive understanding of ChatGPT-based ML models is crucial to see how advancements in science and technology and Artificial Intelligence (AI) can pioneer a whole new set of job opportunities.

While many people are aware of how ChatGPT is giving rise to new job roles. They fail to comprehend how these technologies work. An ability to have a proper understanding of the specialized architecture of the new AI-based technologies prevents you from leveraging them.

Knowledge of machine learning models in the world of AI can come in handy. It allows you to understand the strengths and weaknesses of these technologies so you can know when and how you can use them.

In this article, we will explore different ChatGPT-based ML models and their advantages and disadvantages.

Before, we plunge into the depths of the advantages and disadvantages of machine learning models, we should have a brief overview of what machine learning models are. Moreover, we will also have a short glance at the pros and cons of machine learning models in general. After this, we will look at the advantages and challenges of the GPT-based machine learning models.

What are Machine Learning Models?

A machine learning model refers to a mathematical model which is capable of using algorithms to 'learn' new patterns and employ these algorithms to make predictions. Machine learning models can be used for image recognition, Natural Language Processing (NLP), speech recognition, and fraud detection.

Machine Learning models are capable of performing several tasks. These models are trained on a huge amount of datasets. Due to this, they are capable of doing a variety of tasks, such as:

• Analyzing texts from a wide variety of sources, using the NLP technology.
• Generating text in human-like languages, using the NLG technology.
• Improving the accuracy of machine translation.
• Automating tasks and saving tons of time.

However, like all the nice things, machine learning comes with a tinge of issues. There are currently a few challenges with machine learning models. We have listed the major ones here and a deeper discussion on them will be found in the subsequent sections. The major challenges posed by machine learning technology include:

• Misinformation: The groundbreaking models of ChatGPT may promote misinformation and hate speech.
• Bias: The machine learning models of the chatbot are not trained to eliminate bias.
• Deepfakes: The machine learning models of ChatGPT can create deep fakes by manipulating audio and video recordings.

Machine Learning Models of ChatGPT

The cutting-edge technology of ChatGPT is based on the machine learning architecture called Generative Pre-training Transformer (GPT). Such machine learning technologies operate on Large Language Models (LLMs). In this way, ChatGPT works on NLP technology to analyze and generate language-based data.

Since ChatGPT is a transformer model, it uses a neural network type of machine learning technique. In this type of machine learning, a problem is solved through various processes, such as speech recognition, Natural Language Processing (NLP), and image recognition.

Additionally, ChatGPT's autoregressive language model assists it in predicting the sequence of words. For example, take the sentence "Unstop is India's leading hiring platform". The autoregressive language learning model of ChatGPT will be able to predict that the adjective 'leading' will come before 'hiring' and not the other way around. The choice of placing consecutive adjectives in a sequence requires high-level NLP skills.

Here, we provided a brief explanation of the transformer architecture of ChatGPT. Now, we discuss different machine learning models of ChatGPT and see what are their advantages and challenges.

Learn more about ChatGPT and machine learning technology: Learn How ChatGPT For Machine Learning Works: A Beginner's Guide

GPT-1

ChatGPT-1 was launched in 2018 by the parent company, OpenAI. The architecture of ChatGPT was composed of a 12-level, 12-headed Transformer decoder. It is composed of 4.5 GB of textual data. Along with this, has 117 million parameters, which was comparatively smaller than its successors.

The Advantages of GPT-1

The NLP ability: The ChatGPT-1 version was trained on a huge corpus of language data. As a result of this, it is capable of performing various language-related complex tasks, such as sentiment analysis, text classification, and language translation. It helps the chatbot to analyze and generate a wide range of texts, a few examples of which are scripts, poems, songs, emails, messages, etc.

Multilinguality: ChatGPT is fed with linguistic data from different languages. It was capable of performing language translation tasks in German, French, English, Chinese, and Japanese.

Creative output: The machine learning model of ChatGPT-1 allows it to generate creative human-like texts, such as customized emails, poems, songs, etc. This placed it above other AI-based technologies which could not generate creative responses.

Accuracy: The chatbot, running on the ChatGPT-1 architecture provided the users with accurate responses. The chatbot would provide nearly accurate responses to question-answering.

The Challenges with GPT-1

Biased output: GPT-1 is susceptible to bias, which is reflected in the biases existing in the data it was trained on.

Misleading information: Sometimes, GPT-1 produces text that is inaccurate or deceptive and is not always accurate.

Harmful content generation: GPT-1 is capable of producing negative material like hate speech or false information.

Limitation on language understanding: It cannot understand jokes or sarcasm. It also has problems with creating original compelling content.

GPT-2

It was one of the biggest language models at the time of its launch and a substantial advance over GPT-1 with 1.5 billion parameters. GPT-2 was pre-trained on a sizable corpus of text data that comprised web pages, books, and other written materials. The machine learning model of GPT-2, like GPT-1, was taught to anticipate the following word in a series of words by taking into account the words that came before it. However, GPT-2 displayed a stronger capacity for generalization to new tasks and domains. As a result of this, it was able to produce longer and more cohesive sequences of text.

The Advantages of GPT-2

High-quality text generation: With a wide range of uses GPT-2 is known for its capacity to produce high-quality human-like writing.

Pre-trained models: The pre-trained models included with GPT-2 may be utilized for a variety of applications involving Natural Language Processing, without the need for an additional training process.

Large-scale architecture: The architecture of GPT-2 is built to handle enormous volumes of data, making it appropriate for applications that need to analyze massive datasets.

Flexibility: GPT-2 is tailored to perform a range of Natural Languages Processing tasks, such as question answering, text summarization, and language translation.

The Challenges with GPT-2

Controversial text generation capabilities: GPT-2 has drawn criticism for its capacity to produce false information and news, which has prompted questions about its abuse.

Limited interpretability: Researchers and practitioners who seek to understand how GPT-2 generates its predictions may find it challenging due to the model's complicated underlying design.

Large computational requirements: Due to its enormous model size and intricate design, GPT-2 is challenging to deploy on hardware with constrained computing capabilities.

Language-specific: GPT-2, like other transformer-based models, is largely trained on data in the English language and might not perform as well in other languages.

GPT-3

The next in the GPT family is GPT-3. With 175 billion parameters, more than a hundred times as many as GPT-2, it is one of the most robust and sophisticated language models ever made. With the help of a language modeling assignment, the original GPT-3 model was trained on a sizable corpus of text data that comprised web pages, books, and other written materials. The model was trained to predict the next word in a series of texts given the preceding words in the series, and it was able to produce excellent natural language writing with a high degree of coherence and realism.

The Advantages of GPT-2

A wide range of Natural Language Processing tasks: GPT-3 may be used for creating high-quality content using NLP, such as question answering, text summarization, and language translation.

Production of high-quality text: GPT-3 can produce high-quality, human-like language.

Zero-shot learning abilities: GPT-3 can complete various jobs without needing any prior training, which can save time and money.

The Challenges with GPT-3

Large computational requirements: It is challenging to install GPT-3 on devices with constrained processing resources because of its enormous model size and intricate design.

Language-specific: Like previous transformer-based models, GPT-3 was largely developed using data from the English language, hence it might not function as well when applied to data from other languages without extra training or adjustments.

Limited interpretability: It might be challenging for academics and practitioners to comprehend how GPT-3 generates its predictions because of its complicated design.

Ethical implications: The capabilities of GPT-3 give rise to ethical questions concerning how it may be used inappropriately and the necessity of responsible deployment.

Learn Data Scientists can use ChatGPT: ChatGPT For Data Scientists: The Unmissable Cheatsheet For 2023

InstructGPT

InstructGPT supports this generative AI model and employs reinforcement learning with human feedback to increase its dependability. InstructGPT uses a human feedback method in the fine-tuning process. Humans iterate on a smaller dataset by creating the intended output, comparing it with the GPT output, labeling the GPT output based on human feedback, and presenting the GPT model with that output to help steer it toward the desired outcome on more specific tasks and queries. InstructGPT can outperform GPT-3 because of this procedure, which is now standard inside OpenAI's platform.

The Advantages of InstructGPT

Large model: InstructGPT is based on the 175B parameter GPT-3 language model. As a result, it may produce writing that is more sophisticated and subtle than smaller models.

Increased accuracy: In a number of activities, including text production, machine translation, and question answering, InstructGPT has been proven to be more accurate than smaller language models.

More inventive: InstructGPT has been demonstrated to be more inventive than smaller language models, and it is able to produce more unique and fascinating content.

Less biased content: It has been demonstrated that InstructGPT produces less offensive or hurtful writing than smaller language models.

More effective: When compared to smaller language models, InstructGPT produces text more quickly and with fewer resources.

The Challenges with InstructGPT

Costlier: Compared to smaller language models, InstructGPT is more costly and uses more computational resources during training.
Unreliable: InstructGPT's accuracy is not always reliable, and it occasionally produces content that is false or deceptive.
Misinformation: It is possible to create damaging and derogatory content with InstructGPT, including hate speech and false information.
Lack of originality: InstructGPT occasionally produces content that is dull or repetitive. It is not always inventive.

GPT-3.5

GPT-3.5 is an autoregressive language model that generates text that resembles human speech and was launched by OpenAI in 2020. It will produce text that answers questions when provided a cue.

The design is a decoder-only transformer network with a context that is 2048 tokens long and 175 billion parameters, which at the time required 800GB of storage. Using generative pre-training, the model was trained to anticipate what the next token will be based on the previous tokens. On several tasks, the model showed high zero-shot and few-shot learning.

The Advantages of GPT-3.5

The advantages of GPT-3.5 architecture are the same as its predecessors, such as large datasets, high accuracy, cost-effectiveness, and ease of use.

The Challenges with GPT-3.5

Along with other challenges that were discussed above, GPT-3.5 has additional privacy and security concerns.

Privacy issues: Since GPT-3.5 is trained on a substantial dataset of text and code, privacy issues about the usage of the data are raised.
Security issues: GPT-3.5 is a potent tool that may be used maliciously to produce fake news or disseminate false information.

Learn how to be a prompt engineer: Are You Wondering How To Become A Prompt Engineer In 2023?

GPT-4

GPT-4 was launched in 2023. It is publicly accessible in a limited manner through the chatbot product ChatGPT Plus (a more expensive version of ChatGPT).

The Advantages of GPT-4

Cuts Cost: GPT-4 is a wonderful choice for people and organizations on a tight budget because of its affordability.
More creative: Compared to GPT-3, GPT-4 is more inventive and can produce texts that are more intriguing and original.
Less biased: Compared to GPT-3, GPT-4 has less bias and is less likely to produce offensive or damaging material.
More productive: Compared to GPT-3, GPT-4 is more productive and can produce text more quickly and with fewer resources.

The Challenges with GPT-4

Unreliable: GPT-4 occasionally produces text that is inaccurate or deceptive and is not always reliable.
Offensive content: GPT-4 is capable of producing offensive material like hate speech or false information.
Lack of originality: GPT-4 occasionally produces language that is repetitious or lacks creativity.
High cost: GPT-4 is more costly to train for and utilize than GPT-3 in terms of both cost and processing power.

We hope this article on the ChatGPT machine learning model was informative. We saw how the choice of model can have wider implications.

For more articles on ChatGPT, stay tuned to Unstop.

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