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How Do Large Language Models like OpenAI’s GPT Work?

by Coffee Table Science

The entire world is stunned by the notable wave created by OpenAI’s conversationally interactive Artificial intelligence (AI) ChatGPT, a Large Language Model (LLM) that exceeded one million users within the first week of its launch. It has set up a foundation for advanced machine learning and its application in the existing world.

Large Language Models

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Google’s BARD with Microsoft and NVIDIA’s Megatron-Turing NLG are in the race of proving to build a deep learning infrastructure and get their technical supremacy in the natural learning process. So, what is deep learning Large Language Models that are considered the future of the tech world? Let’s have a broader knowledge of natural language processing (NLP)-driven Large Language Models.

Natural Learning Processing in Artificial Intelligence

Language is one of the best ways of communication humans have created. It allows us to speak, understand and respond as other creatures do. So in machines, we wanted to generate a more human-like interactive approach that could understand and answer the same way we can.

That’s where natural language processing (NLP) works. It is the unit of artificial intelligence that gives computers the ability to read written texts or spoken words,  interpret and respond the same way a human does.

Computers have programs to read and microphones to hear (collect) the audio while some programs help them process the information and interpret it. During input processing, the computer changes the human input into the coding language it understands.

The main algorithms that boost a computer’s efficiency to process human language and analyze its meaning while keeping the writer’s intent are:-

  • Rules-based system:- This approach comprises linguistic or grammar-based rules. to store, sort and manipulate data in artificial intelligence. They need a source of data and a set of rules.

  • Machine learning-based system:- In Machine learning algorithms the computers understand and learn to perform the tasks without being programmed. They acquire knowledge depending on their training data and produce their own rules.

Natural Learning Processing (NLP)

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  • Deep learning:- Deep learning is a subset of machine learning that is based on artificial neural networks that understand information and use them to make human brain-like decisions. It requires a large dataset and learns from its own mistakes and increases its accuracy by continuously evolving.

What are Large Language Models?

Large Language Models (LLMs) are AI models that recognize, understand, and generate texts. They are deep-learning complex algorithms trained with enormous datasets to generate a human-like text, answer questions, summarize documents, and compute. In machine learning, a dataset is a collection of data required to train the language models. It can include text data, image data, audio, numerical, and videos to teach machine learning algorithms to predict the successive words in a sentence or phrase.

Generally, they are humongous sizes (tens of gigabytes) and trained on enormous (at the petabyte scale, where one petabyte=1,000Terabytes) datasets. Large Language Models rely on a complex algorithm and develop a pre-trained model for the range of tasks. LLM relies on deep learning neural networks to conquer the natural language.

LLMs are among the most prominent parameter count models. Parameter refers to an independent ability to evolve as it keeps learning. It makes them even more remarkable to handle various tasks with more accuracy. Megatron-Turing NLG  trained with 530 Billion parameters, released in the year 2022.

Large Language Models Parameter

Image Credit: Hugging Face

World’s widely accepted search engine, Google, is the testimony of the reputed LLM called BERT (Bidirectional Encoder Representations for Transformers). Google BERT helps Google search to understand the disambiguate in phrases where polysemic (multiple possible meanings) nuance could change the complete meaning. Ex, the clear differentiation between ‘two’ and ‘to.’ChatGPT-3 (Chat Generative Pre-trained Transformer) is potent to handle several linguistic tasks, generating high quality, grammatically correct definitions, essays, and codes to provide a natural language interface to various other applications improving customer management. 

A natural language interface is the interface where a human or user and a machine communicate through a natural language system. The human/user provides input in the form of text or speech, and the model generates a response in the form of speech and text uttered.

How does LLM Work?

Large Language Models work on deep learning neural networks and processes on NLP. Neural networks are networks of artificial neurons connected in layers. These layers can receive an input from adjacent layers and produces an output. The output depends on its weight, which is adjusted while the model is trained. 

The Large Language Models are composed of multiple layers of neurons. The first layer works as input and takes in a sequence of words. The subsequent layers function to process the output of previous layers in the model. The result of the last layer is the interpretation or prediction of the language model.

LLMs use recurrent neural networks (RNN) to work collectively and predict the forthcoming word in a sentence. A recurrent neural network is a subset of artificial neural networks that commonly work as speech recognition and natural language processing to predict the next coming suitable word in a sentence.

The amount of information is directly dependent on the size of the datasets. Further, a large-sized dataset ensures the accuracy and productivity of the output. Large Language Models are more sophisticated NLPs and consider various other texts to understand a context accurately. 

For example, if a sentence reads, ‘A girl is walking at the bank of the river,’ LLM can understand the context of ‘bank’ and its reference to the given sentence. Further, it can differentiate the word ‘bank’ that refers to a river shore from money-keeping ‘bank.’ This ability to interpret the data is a milestone in its data analysis, sentiment analysis, and generating a meaningful context. 

How Are LLMs Trained?

Large Language Models trained in two stages-

Large Language Models Training

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(a)  Pretraining- This is the first stage where language models learn semantics, grammar, and structure with exposure to billions of examples. This stage is the most important and expensive process to feed the models with almost every text, knowledge, Wikipedia, etc. Large models learn faster due to their enormous dataset capabilities.

(b)  Fine-tuning-  In this stage, the large models practically approach their learning. They solve tasks such as question answers, sentimental analysis, generating texts, document summarization, and more.

Applications Of Large Language Models In Various Sectors

Large Language Models have applications in various industries, including biology, technology, and customer management.

  • Machine translation:- It translates one language to another using machine. Meta has conducted various studies for 204 languages to improve machine learning. This number is far more eminent than ever been translated at one time. 

  • Speech recognition:- Speech recognition involves processing audio speech and responding accordingly. Ex: Alexa and Siri.

  • Sentiment analysis:- Sentimental analysis can understand the sentiments behind a particular phrase or sentence. Further, it works to know the opinions and reviews to express a given text. It has more demand in the business platforms where it analyzes product reviews, customer reports, and surveys on a small scale. Ex: BERT.

  • Molecular biology:- NVIDIA BioNeMo is a domain-specific framework for proteomics, RNA, and DNA. 

  • Customer management:- The customer service management sectors will benefit from interactive chatbots and AI. Marketers can easily organize customer feedback, reviews, and questions based on a product’s description and service.

  • Text generation:- The capability of Large Language Models to generate a whole sentence based on a few input words is really commendable. It is what ChatGPT does.

  • Code generation:- Codex (powering Copilot) is the very first approach that comes to our mind. 

  • Chatbots:- They enhance productivity and comfort in customer care services. LaMDA, Rasa, and Cohere are among the best power chatbot tools.

  • Grammar correction:- Grammarly, Writer.com, and Duolingo are among the list to provide appropriate grammar correction and styling.

  • Response Generation:- This is the approach of promoting dialogue using sample talks and machine learning. It is known as predictive dialogue, where the extent of the next word is the dependent model, with users’ responses.

Limitations Of Large Language Models

Even after various advantages, LLM has certain limitations. They can provide misleading and false information that harms certain groups of society. These can deliver a biased output and is a concern for natural learning.

Further,  LLM demands deep pockets to conduct the research and implementation of the models’ For Ex: ChatGPT-3 is a model with over 175 billion machine learning parameters, and Megatron-Turing NLG with 530 billion parameters. 

They are negatively impacting our environment. Ex: Megatron-Tuning was developed using hundreds of NVIDIA DGX A1100 multi-GPU servers, each using up to 6.5 kilowatts of power supply. Such enormous frameworks need more power to cool and leave behind large carbon footprints.

Sum It Up!

To sum it up, this comprehensive article has explained the Large Language Models (LLM) simply and crisply. You can read the complete article to know how these large models actually work and how they get trained. Read this exclusive article to understand other essential terms and their reference to the main keyword, LLM, and their limitations. If you have any questions, freely drop them in the comment section. We will help you out!

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