ChatGPT as a Large Language Model (LLM): Properties and Peculiarities

Since its advent in November 2022, ChatGPT has already earned 180 million users, and this number is increasing rapidly. The buzz around it is totally justified—the ChatGPT AI model can do a lot: write an essay, help with coding, fix grammar mistakes and make the text more accessible, brainstorm ideas, and many other things. 

But how does ChatGPT manage to do all of this? The answer lies in its underlying technology — LLM, or Large Language Model. LLM is a cutting-edge technology that uses advanced algorithms to analyze and generate text in natural language, just like humans. LLM is a relatively unknown term, but this article will provide a glimpse of what LLM is and how it contributes to the all-favorite ChatGPT. 

What is the ChatGPT model?

Let’s get back to a familiar notion—what exactly is ChatGPT? It is an artificial intelligence assistant that was developed by the company OpenAI. ChatGPT model was trained on large datasets of human texts, which helps it provide human-like results. The interaction process is simple—you ask ChatGPT a question and get an answer. Most people are familiar with ChatGPT-3 and ChatGPT-4, upgraded versions of ChatGPT LLM, which display a more comprehensive array of services, like high-quality text and image generation. 

What does GPT stand for?

ChatGPT model is a specific instance of GPT. GPT is an abbreviation that can be broken down like this: 

G	Generative —  It is intended to generate a wide range of content, from poems and essays to video scripts.
P	Pre-trained — Before being utilized, the model undergoes rigorous training on a vast dataset. As a result, it has a broad base of knowledge and language comprehension.
T	Transformer — It is a unique deep-learning architecture that helps better understand contextual cues.

What is a Large Language Model (LLM)?

LLM is like a Megamind that knows the answer to every question. It is possible because it takes advantage of deep learning and the branch of neural networks known as transformers. It can perform various tasks, mainly connected to NLP (Natural Language Processing), including summarizing, translating, and paraphrasing content. With such functionalities, LLM is an excellent asset for anyone in the content sphere — copywriters, SEO specialists, and content creators. These models can also come in handy for customer service, as chatbots also have to process a lot of data and quickly provide the best answers for the clients. 

The “large” in LLM refers to the large datasets (some of which might have millions or more parameters) that the models process to gain a complete understanding of human language. LLMs are divided into four different types based on their capabilities: 

1. Language representation models are advanced neural networks specifically designed to process and comprehend human language. These models are trained on vast amounts of text data to understand the intricacies of human syntax, grammar, and context. 
2. Zero-shot models are a type of machine learning model that can perform multiple tasks simultaneously and generalize their knowledge to new, unseen tasks. This ability to operate without pre-training makes them highly efficient and versatile, and they have become increasingly popular in natural language processing and computer vision applications.
3. Few-shot models are a type of large learning model that can quickly learn a new task with only a few examples. They are becoming increasingly popular in machine learning, especially in areas where obtaining large amounts of training data might be difficult or costly.
4. Multimodal models can handle and produce outputs using various modalities, such as text, images, and audio. They can combine information from different modalities to complete tasks like generating captions for photos, comprehending videos, and performing multimodal translation. 
5. Fine-tuned or domain-specific models are trained for particular industries or fields. These fields may include legal text analysis, medical literature understanding, scientific research, etc. 

The field of large language models is constantly evolving, with new versions and applications frequently emerging. If you’re interested in exploring LLM models other than OpenAI’s ChatGPT, you can take advantage of several well-known models: 

• Google Gemini — This cutting-edge AI model was created to compete with ChatGPT LLM. It is a multimodal model that can perceive any input (text, image, or video) and provide any output. It has excellent potential to be used for various industries and applications, from natural language processing and computer vision to robotics and autonomous systems.
• Google Bert — This language model stands for Bidirectional Encoder Representations from Transformers. BERT has become a popular tool in natural language processing and is used for various tasks, including question-answering and sentiment analysis.
• Google PaLM — It stands for Pathway Language Model. It utilizes a multi-path approach, which handles and analyzes text in parallel, much like the human brain, enabling it to process information more efficiently and accurately.
• Meta LLaMA, or Large Language Model Meta AI) — It is a large language model created by Meta AI. LLaMA is a more efficient and resource-light alternative to the ChatGPT model, requiring less computational power and a smaller size.
• XLNet — This model stands for eXtreme Language understanding NETwork and was developed by Google AI. This model uses permutation-based training, which is quite innovative compared to other LLMs. For example, Google Bert hides some words during training and predicts them based on the context of the surrounding words, which poses some limitations. In contrast, XLNet learns the dependencies between all words, regardless of their position in the sequence.

Despite all the good things that large language models offer, some challenges still occur. Here are some of them: 

• LLM can be biased — Language models heavily depend on the data they are being trained on. If the data is not diverse and contains some preconceptions and biases, it will be reflected in the outputs. 

• Development and maintenance can be costly — Creating your own LLM can take a lot of money and resources, which is why most companies rely on models like GPT or Google Gemini. 
• Hallucinations — The output generated by large language models might need double-checking, as the data utilized for training can be inaccurate, or the model might misinterpret the contextual cues. 
• Security — As large language models have to process vast amounts of data, sensitive details included, there might be concerns about how secure the models are. Will they withstand security breaches and data leakages? The question remains open. 
• Environmental influence — Large language models consume a lot of energy when training and storing data. This energy consumption harms the environment, leading to increased carbon emissions and other harmful pollutants. Thus, it is essential to consider the environmental impact of large language models when developing and using them.
• Consent — There are some ethical concerns regarding the data that LLM processes. There is no guarantee that individuals have given explicit and informed consent for their data to be collected, stored, and used by LLM. Maintaining transparency and respect for people’s privacy and autonomy is crucial.

Is ChatGPT an LLM?

Yes, ChatGPT belongs to the LLM family because of the number of features it shares. Let’s take a look at what binds them: 

• Transformer architecture — LLM and ChatGPT models are constructed using the transformer architecture, which has demonstrated remarkable success in natural language processing tasks.
• Generation ability — LLM and ChatGPT models can generate text relevant to the context and prompt provided, which is ideal for text summarization, creative writing, and completion tasks.
• A large number of parameters — LLM and ChatGPT have a vast number of parameters, which could range from hundreds of millions to billions. Their high parameter count is instrumental to their capability to grasp complex patterns in natural language data and produce high-quality text output.

Key Properties in GPT-3 

Recently, ChatGPT-3 has gained much popularity and has become a buzzword among various professionals. This AI-powered language model has proven to be a game-changer for people from different fields, such as students, writers, IT specialists, and developers. Let’s look at the prominent features of GPT-3 as an LLM: 

• Zero-shot learning — No pre-training is needed for GPT-3 to provide answers to some questions. 
• Few-shot learning — GPT-3 can make a decision and provide an output based on a few examples it has processed before. 
• Question answering — GPT-3 does not retrieve information from the databases and put it out without any changes but tries to compose it to fit the question perfectly.
• Code generation — Though the ChatGPT language model cannot surpass the coding skills and creativity of the developers, it still can produce excellent results. With clear and concise prompts, you can achieve usable code or suggestions for improving it. 
• Chain-of-thought-based reasoning — When examples are not enough to find a solution, GPT-3 can explain how the problem was approached and solved and, in elaborating, find the answer. 

What is the Difference Between ChatGPT and Other LLMs? 

ChatGPT and other large language models have much in common, as they belong under the same umbrella of LLM. But what makes ChatGPT stand out? First, the ChatGPT model is an excellent example of an intelligent chatbot that can conduct productive human-like conversations with users. This is because it has been trained on large datasets of conversational data.  

The ChatGPT language model handles conversations more effectively than other language learning models. It has been specifically tailored to maintain dialogue context so that the responses are coherent and relevant, even as the conversation progresses over multiple turns. This unique feature distinguishes ChatGPT from other LLMs that may prioritize single-turn responses or task-specific contexts.

Another feature that makes the ChatGPT model stand out is its use of zero-shot and few-shot learning. Other LLM models might need more pre-training to generate outputs.

Wrapping up

Since LLM and deep learning have entered our picture, we can already see the positive changes in how we perform our jobs. Thanks to these technologies, we can now complete tasks in a smaller fraction of the time than it would have taken us before, with far fewer errors. Not only that, but we have also reached a new level of engagement with technologies. Now, we can converse with the ChatGPT model as if it is our assistant or a good pal helping us with our tedious assignments (just remember to say “please” to ChatGPT to be on good terms with it in case AI takes over). 

If you want to learn more about LLM and its applications, we invite you to contact our company, LITSLINK. We have a team of experts in AI development who can provide you with the best solutions and help you leverage the power of these technologies to achieve your goals.