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Exploring Large Language Models: How They're Shaping the Future of Communication

Last updated on: 
January 5, 2024
Ashwin Ramachandran
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About The Author!
Ashwin Ramachandran
Ashwin Ramachandran
Head of engineering at Interview Kickstart. Playing in the field of technology with the tools of Java for more than a decade. A mind full of innovative ideas and curiosity for exploring data.

Have you ever wondered what algorithm is used in ChatGPT? Almost everyone is familiar with ChatGPT or may have used it at least once by now. ChatGPT is an AI chatbot based on LLM (large Language models). Recent research shows that the market value of LLM will reach $40.8 Billion by the year 2029. In the AI world, LLM has revolutionized language models with its capability to handle large datasets. 

Here’s what we’ll cover in this article: 

  • What is LLM (Large Language Models)?
  • Transformer Model in Large Language Models
  • Working Mechanism of Large Language Models
  • Use Cases of Large Language Models
  • Examples of LLMs (Large Language Models)
  • Wide Range of Applications of Large Language Models
  • Challenges and Limitations of Large Language Models
  • Future of NLP and Large Language Models
  • Unlock Your Potential Career in AI ML at Interview Kickstart
  • FAQs on Large Language Models

What is LLM (Large Language Models)?

The large language model, also known as the foundation model, is an AI algorithm that analyzes large datasets with the help of statistical models to mimic human intelligence. It is a deep-learning algorithm that can be used in NLP advancements to perform a variety of tasks. LLMs can summarize, analyze, translate, and predict text as it has billions of parameters.

Large language models are the base of generative AI, as All generative AI applications are based on LLMs. LLMs use a transformer model (transformers), basically neural networks, which are akin to the neurons in our brains. Due to neural networks, which are layered nodes and millions of parameters (variables), which are like a vast knowledge library, the large language models can understand and predict quick results and hence are used in many NLP (Natural Language Processing) applications, such as chatbots and language models.

LLM in AI and ML

Transformer Model in Large Language Models

In machine learning, transfer learning is a technique used to absorb and learn knowledge from a specific task. This knowledge is re-used and utilized to enhance the efficiency of other tasks in a domain. The most common architecture used to implement large language models is the transfer model

In the transformer neural network process, the pre-trained LLM model acquires the skillset and knowledge of a task and implements this information on other related tasks. The transformer model architecture does the input tokenization to process large amounts of data. It performs mathematical computations and identifies the connections and relationships of tokens with its self-attention mechanism and contains an encoder and decoder. 

The self-attention mechanism in large language models uses and analyzes the detailed sequences of sentences and sometimes the entire text to accurately give predictions.

Transformer model architecture in LLM
Harvard University

Working Mechanism of Large Language Models

LLM model works on the basis of transformer model architecture. The architecture process predicts the output by the input encoding-decoding process. The LLM model requires training before receiving any text input in order to predict the output. Let’s look at the step-by-step process of training and working on large language models:

  1. Data Gathering

To train the LLM models, the first step is to collect a large amount of textual context data to learn from pre-existing resources for more refined output. The diverse range of resources may include website content, articles, books/ E-books, research papers, or any other textual data. The comprehensive dataset can help LLM to understand the text and language better.

  1. Tokenization Process

Once the data collection is done, the collected data is broken into small pieces or units known as tokens. Tokens can be any piece of text, such as a character, a word, subwords, etc., based on the different models. 

  1. Pre-Training Process

After tokenization, the LLM model enters into the pre-training phase, where it learns from the tokenized text data from the above step. Here, the LLM model learns to predict the successor token in the sequence with the help of the previous token. It is an unsupervised process using unlabelled data, which enhances the LLM model’s pattern understanding, semantics, and grammar and learns the different meanings of the same words like “live.”

  1. Transformer Model Architecture

The transformer model is the most common architecture used in LLM models. It is a self-attention mechanism that computes the attention score based on each character and word in a sentence and how it interacts with other words. It helps LLMs to focus specifically on the most relevant text and information because different weights are assigned to different words.

  1. Fine-Tuning

Fine-tuning is used to optimize a specific task. The fine-tuning process provides task-specific labeled data to the LLM model. It helps the model to match its output to the expected outcome.

  1. Inference Process

After fine-tuning, the LLM model can do language-specific tasks and generate text with the help of inference. Now, it can use the learned knowledge to generate an efficient response. In the inference process, LLMs use a beam search algorithm to explore certain paths to generate related sequences of tokens to generate precise text outputs.

  1. LLM Contextual Understanding

Large language models can use the knowledge of the input sequence to generate preceding context text. As it is utilized for AI-driven communication, LLM is best at analyzing the context of the text and giving contextually accurate responses.

  1. Output/ Response Generation

LLM predicts the response with the help of the preceding token in a sequence. It uses contextual knowledge to generate high-quality, creative, relevant, human-like responses to the language.

Use Cases of Large Language Models

LLMs are emerging as one of the top choices for communication AI models because of their wide range of uses and applications to do NLP tasks with ease. Use cases of LLMs are as follows:

  • Sentiment Analysis: LLMs can be used to perform sentiment analysis to understand the intent behind the piece of textual data accurately.
  • Text Generation: Today, everyone is experiencing GPT technology impact. The generative AI uses LLMs to generate textual output in models like ChatGPT. For example, give a prompt “Best places to visit in XYZ city” to generate the desired response.
  • Chatbots and Language Models: LLMs are capable of enabling conversational AI or chatbots to communicate with users and answer their queries.
  • Content Translation: LLMs can classify, categorize, re-write, and summarize the content. It is also trained to translate content from one language to another.
  • Custom Chatbots: Nowadays, many companies and websites can use custom chatbot models to communicate with their customers for better customer engagement.
  • Query Retrieval: LLMs are used in search engines to retrieve the asked information from millions of resources to give you a relevant response. It summarizes the output and communicates with the users in a conversational tone.

Examples of LLMs (Large Language Models)

Some of the most popular models that have revolutionized AI-driven communication are as follows:

  • BERT Model

Google developed BERT (Bidirectional Encoder Representations from Transformers). BERT is a transformer-based model that specializes in tasks such as Q&A, sentiment analysis, and better language understanding.

  • XLNet Model

XLNet (eXtreme Language Understanding) was developed by Google and researchers from Carnegie Mellon University. It generates output responses in random order, which makes it different from other models like BERT. It predicts the succeeding tokens in random order instead of sequence.

  • GPT Models

GPT models developed by OpenAI are Generative AI models, which are pre-trained transformers. These are some of the most famous large language models. GPT-4 is the advanced version of previous GPT-3 and GPT-3.5 models. It can be fine-tuned to the user’s needs to perform specific tasks and integrated with the existing system of your software.

  • Turing-NLG Model

It is a robust and powerful LLM developed by Microsoft to generate conversational outputs to the queries. It is trained on large datasets to recognize patterns and generate interactive responses in context with the communication.

Wide Range of Applications of Large Language Models

LLMs have a diverse range of applications in different fields and industries due to their versatile approach to understanding the language. A wide range of applications of LLMs are as follows:

  • Healthcare Sector: LLMs can be helpful in finding cures for serious diseases. They have a deeper understanding of proteins, molecules, DNA, etc., which can be useful in biological research and vaccine development.
  • Tech Domain: They can generate fast responses to queries and can be successfully embedded in search engines. They can help the programmers to generate precise code as well.
  • Customer Service and Marketing: The marketing sector can use sentiment analysis to its advantage by quickly creating social media campaigns with the help of LLMs. 
  • Customer Experience: The customer service of any business is enhanced with the help of conversational AI integration into the websites.

The LLM applications are not restricted to the above uses. They can be used in the banking sector, legal sector, academic research with ethical means, etc.

Challenges and Limitations of Large Language Models

Despite being an advanced tool for response generation, there are certain limitations and challenges faced by LLMs, which are as follows:

  • Faulted Output: It is when an LLM model generates inaccurate and false output to the query, which is also known as hallucinations. It can predict and generate responses to the next word relevant to the previous word but is incapable of interpreting human-like emotions.
  • Biased Outputs: The generated response is completely based on the data given to train the model. Due to the lack of diverse data demographics, it may generate biased outputs because it is trained that way.
  • Security Risk: LLMs should be managed ethically. Otherwise, it can be a threat to security, such as leaking private information that can be used for many scams and unethical deeds on the internet.

Future of NLP and Large Language Models

ChatGPT is a Natural language processing model based on the advanced LLM. It has taken the world by storm with advanced features. There are many possibilities for future advancements for large language models.

One thing is certain: LLMs will strive to be more refined and “wise” in terms of generating outputs to mimic realistic human intelligence. As the LLM applications expand over different business sectors, it will open a sea of job opportunities for the upcoming generation. 

If LLM models are used ethically, the future looks bright with more accurate, effective, and efficient outputs. The future LLM models will most probably be trained on much larger parameters and datasets to reduce the biased outputs and give more responsive results to the questions.

Unlock Your Potential Career in AI ML at Interview Kickstart

Whether in the real world or the technical domain, language is the base that conveys messages and enables us to communicate. Large language models are one of the many models and algorithms used in the field of AI and ML. The career opportunity spectrum of ML is becoming broad. Choose the exclusive machine learning courses that sharpen your knowledge alongside the interview questions curated by our subject experts to help you land your dream job.

Unlock your path to a successful career by joining Free-webinar at Interview Kickstart!

FAQs on Large Language Models

Q1. How are the language models trained?

The language models are trained on a wide range of textual datasets, such as research papers, books, articles, etc., to understand the grammar and semantic patterns. Large datasets help the LLM models learn the connection between joining words and how to predict accurate responses based on the training.

Q2. Why do large language models make mistakes in prediction?

LLMs hallucinate or make mistakes in predicting output when the parametric knowledge of the datasets during the pre-training phase conflicts with the contextual knowledge. It can happen due to the restrained abstract information while training the model.

Q3. How do you evaluate the accuracy of a large language model?

The performance of any LLM is evaluated based on different factors, such as fluency in the language, coherent responses, accurate contextual understanding of data, fewer anomalies, and more accurate data based on facts, desired outputs, etc. 

Q4. Are LLMs probabilistic models?

Yes, the large language models are based on probability algorithms, where they try to make the blueprint of probability by connecting words and characters to make sentences.

Q5. What is the difference between the LLM and NLP model?

LLM is a subfield of NLP. NLP models cover a large field of tasks, from semantic analysis to text recognition. In contrast, LLMs are the most developed field of NLP, which can do multiple tasks such as text generation, text completion, context generation, etc.

Posted on 
January 3, 2024

Ashwin Ramachandran

Head of Engineering @ Interview Kickstart. Enjoys cutting through the noise and finding patterns.

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