What are Large Language Models(LLMs)?

A large language model is like a computer whiz that learns and talks like a human, thanks to a special architecture called a transformer. It’s trained on tons of written stuff.

These models, known as Large Language Models (LLMs), are the backbone of cool tech that uses deep learning to make sense of human language. They soak up massive amounts of text to pick up on how words and ideas fit together. LLMs can do all sorts of language tricks, like translating between languages, figuring out if text sounds happy or sad, chatting like a pro in a conversation, and more. They’re like language superheroes that can understand fancy text, spot connections between things, and even whip up new sentences that make perfect sense.

Learning Objectives

  • Understand the concept of Large Language Models (LLMs) and their importance in natural language processing.
  • Know about different types of popular LLMs, such as BERT, GPT-3, and T5.
  • Discuss the applications and use cases of Open Source LLMs.
  • Hugging Face APIs for LLMs.
  • Explore the future implications of LLMs, including their potential impact on job markets, communication, and society as a whole.

This article was published as a part of the Data Science Blogathon.

What is a Large Language Model (LLM)?

A large language model is an advanced form of language model developed through deep learning techniques and trained on extensive text datasets. These models exhibit the ability to generate text that closely resembles human language and excel in various natural language processing tasks.

In contrast, the term ‘language model’ generally encompasses the idea of assigning probabilities to word sequences based on the analysis of textual corpora. The complexity of a language model can vary, ranging from simple n-gram models to more intricate neural network models. However, when we refer to a ‘large language model,’ we typically mean models that leverage deep learning methodologies and boast a substantial number of parameters, often ranging from millions to billions. Such models excel in capturing intricate language patterns, producing text that is frequently indistinguishable from human-authored content.”

what is LLm
What are Large Language Models(LLMs)? 5

LLM Explained: Buddy’s Tale: A Heartfelt Dive into Language Models

Hey there, readers! Let’s dive into the heartwarming story of Peter Pandey and his feathered companion, Buddy. Imagine Buddy, this amazing parrot, chilling with Peter and mimicking their daily chit-chat. It’s like a cute parrot version of predictive text magic! When Buddy hears “feeling hungry,” it’s like he’s picking from a menu in his birdie brain – Biryani, cherries, or just good ol’ food. The catch? He doesn’t get what these words mean, he’s just throwing them out based on past convos. We call him our “stochastic parrot” – a fancy way of saying he’s got a knack for randomness and probability.

Now, let’s connect the dots to Language Models (LLMs). They’re like Buddy, but instead of mimicking Peter, they predict the next set of words using neural networks – think super-smart computer programs. Just like Buddy has his ears tuned to Peter’s home convos, LLMs are trained on massive datasets, like all the cool movie stuff on Wikipedia. Ever used Gmail autocomplete? That’s one of the many tricks LLMs have up their sleeves.

But wait, there’s more! Buddy gets a divine upgrade – he can now eavesdrop on global chats, just like LLMs trained on Wikipedia, Google News, and whatnot. These models are like the rockstars of prediction, thanks to trillions of parameters in their neural networks. It’s like Buddy becoming the town’s ultimate conversational guru, giving history lessons, nutrition tips, and even writing poetry!

But, here’s where it gets real, Buddy unintentionally picks up some not-so-great stuff. Just like LLMs sometimes catch toxic language. Peter steps in, guiding Buddy away from the negativity, which mirrors how humans help LLMs be less toxic. It’s like a real-life reinforcement learning with human feedback (RLHF) gig.

However, our tale has a heartfelt twist – LLMs, as powerful as they are, don’t have the feels we humans do. They’re pure data maestros, lacking the depth of emotions or consciousness. So, while Buddy’s story is a cute analogy, the techy side of LLMs is a bit more complex.

Join us on this journey of understanding – from Buddy’s playful mimicry to the intricate world of LLMs. It’s a symphony of words, a dance of prediction, and a peek into the magic of our digital landscape. If you’re vibing with this, share it with your curious pals! 🌟

How a Large Language Model (LLM) Is Built?

A ‘large language model,’ often implemented as a large-scale transformer model, is typically too substantial to execute on a single computer. As a result, it is made available as a service through an API or web interface. These models undergo training using extensive text data derived from diverse sources, including books, articles, websites, and various other written content forms. Throughout the training process, the models analyze statistical relationships between words, phrases, and sentences. This enables them to generate responses to prompts or queries that are both coherent and contextually relevant.

ChatGPT’s GPT-3, a large language model, underwent training on extensive amounts of internet text data, providing it with the ability to comprehend various languages and possess knowledge across diverse topics. Consequently, it can generate text in multiple styles. Although its capabilities, such as translation, text summarization, and question-answering, may appear impressive, they are not unexpected, as these functions operate using specific ‘grammars’ that align with given prompts.

How do large language models work?

Large language models, such as GPT-3 (Generative Pre-trained Transformer 3), operate based on a transformer architecture. Here’s a simplified explanation of how they work:

  1. Learning from Abundant Text: These models commence their process by assimilating a vast amount of text sourced from the internet, akin to learning from an extensive library of information.
  2. Innovative Architecture: Utilizing a distinctive structure called a transformer, they can comprehend and retain substantial amounts of information effectively.
  3. Word Breakdown: The models dissect sentences into smaller components, breaking down words into pieces. This approach enhances their efficiency in working with language.
  4. Understanding Words in Context: Differing from simple programs, these models grasp individual words and understand how they relate to each other within a sentence. They capture the entire context.
  5. Specialization: Following general learning, these models can undergo additional training on specific topics to excel in particular tasks, such as answering questions or generating content on specific subjects.
  6. Task Execution: When presented with a prompt, be it a question or instruction, these models leverage their acquired knowledge to respond. It’s akin to having an intelligent assistant capable of understanding and generating text.

Difference Between Large Language Models and Generative AI:

Generative AI is akin to a vast playground filled with a variety of toys for crafting new creations. Within this space, it can craft poems, compose music, generate images, and even invent novel concepts.

Large Language Models (LLMs) act as the premier word builders within this expansive playground. They excel at skillfully employing words to craft stories, translate languages, respond to questions, and even generate code.

In essence, generative AI represents the entirety of the playground, while LLMs serve as the language experts, proficiently navigating and utilizing words within this creative space.

General Architecture

The architecture of Large Language Models primarily comprises multiple layers of neural networks, including recurrent layers, feedforward layers, embedding layers, and attention layers. These layers collaborate to process input text and generate output predictions.

The embedding layer transforms each word in the input text into a high-dimensional vector representation. These embeddings capture both semantic and syntactic information about the words, enabling the model to comprehend the context effectively.

The feedforward layers of Large Language Models consist of multiple fully connected layers that apply nonlinear transformations to the input embeddings. These layers facilitate the model in learning higher-level abstractions from the input text.

The recurrent layers in LLMs are designed to interpret information from the input text sequentially. These layers maintain a hidden state updated at each time step, allowing the model to capture dependencies between words in a sentence.

An essential component of LLMs is the attention mechanism, enabling the model to selectively focus on different parts of the input text. This mechanism helps the model attend to the most relevant portions of the input text, enhancing the accuracy of its predictions.

Examples of LLMs

Let’s explore some well-known Large Language Models (LLMs):

GPT-3 (Generative Pre-trained Transformer 3):
Developed by OpenAI, GPT-3 stands out as one of the largest Large Language Models, boasting an impressive 175 billion parameters. It exhibits versatility in tasks such as text generation, translation, and summarization.

BERT (Bidirectional Encoder Representations from Transformers):
Created by Google, BERT is another widely recognized LLM. Trained on an extensive corpus of text data, BERT excels in understanding sentence context and generating meaningful responses to questions.

Developed jointly by Carnegie Mellon University and Google, XLNet employs a unique approach to language modeling known as “permutation language modeling.” It has achieved state-of-the-art performance in various language tasks, including language generation and question-answering.

T5 (Text-to-Text Transfer Transformer):
Google’s T5 is a versatile LLM trained in a range of language tasks. It excels in text-to-text transformations, such as translating text into another language, creating summaries, and answering questions.

Roberta (Robustly Optimized BERT Pretraining Approach):
Developed by Facebook AI Research, Roberta represents an enhanced version of BERT, showcasing superior performance across multiple language tasks.

Open Source Large Language Model(LLM)

The advent of open-source Large Language Models (LLMs) has brought about a revolution in the field of natural language processing. This development has simplified the process for researchers, developers, and businesses to construct applications that harness the capabilities of these models to build products on a large scale without incurring costs. A notable example is Bloom, which stands out as the first multilingual LLM trained with complete transparency. It represents the largest collaboration of AI researchers ever involved in a single research project.

Boasting an impressive 176 billion parameters, surpassing OpenAI’s GPT-3, Bloom exhibits the capability to generate text in 46 natural languages and 13 programming languages. Its training dataset encompasses 1.6 terabytes of text data, equivalent to 320 times the complete works of Shakespeare.

Bloom Architecture

 Bloom Architecture| large lanuage model (LLM)

The architecture of Bloom shares similarities with GPT-3, as it follows an auto-regressive model for next token prediction. However, Bloom distinguishes itself by being trained in 46 different languages and 13 programming languages. Its architecture features a decoder-only design with multiple embedding layers and multi-headed attention layers.

Bloom’s architectural design is well-suited for training in diverse languages, enabling users to seamlessly translate and discuss various topics in different languages. We will explore examples of these capabilities in the code below.

In addition to Bloom, there are other Large Language Models (LLMs) available, and we can leverage the Hugging Face APIs to connect to pre-trained models such as Bloom, Roberta-base, and more.

Hugging Face APIs:

Let’s delve into how Hugging Face APIs can assist in generating text using LLMs like Bloom and Roberta-base. To get started, sign up for Hugging Face, and obtain the API access token. After signing up, navigate to the profile icon on the top right, click on settings, and then Access Tokens.

 Hugging Face Token| large lanuage models

Example 1: Sentence Completion

Now, let’s explore how we can employ Bloom for sentence completion. The code snippet below utilizes the Hugging Face API token to make an API call. The input text and appropriate parameters are included to request the optimal response.

# Import necessary libraries
import requests

# Define the Hugging Face API endpoint
api_url = "https://api.huggingface.co/models/Bloom/sentence-completion"

# Input text for sentence completion
input_text = "The quick brown fox"

# Set parameters for the API call
params = {
    "text": input_text,
    "max_tokens": 50,  # Adjust as needed for the desired length of completion
    "temperature": 0.7  # Adjust the temperature parameter for response variability

# Hugging Face API request
response = requests.post(api_url, params=params)

# Extract and print the completed sentence
completed_sentence = response.json()["choices"][0]["text"]
print("Completed Sentence:", completed_sentence)

In this code, replace the api_url with the actual API endpoint for Bloom’s sentence completion, and adjust the input_text, max_tokens, and temperature parameters based on your specific requirements. The completed sentence will be printed as the output.

import requests
from pprint import pprint

API_URL = 'https://api-inference.huggingface.co/models/bigscience/bloomz'
headers = {'Authorization': 'Entertheaccesskeyhere'}
# The Entertheaccesskeyhere is just a placeholder, which can be changed according to the user's access key

def query(payload):
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.json()
params = {'max_length': 200, 'top_k': 10, 'temperature': 2.5}
output = query({
    'inputs': 'Sherlock Holmes is a',
    'parameters': params,


Temperature and top_k values can be modified to get a larger or smaller paragraph while maintaining the relevance of the generated text to the original input text. We get the following output from the code:

[{'generated_text': 'Sherlock Holmes is a private investigator whose cases '
                    'have inspired several film productions'}]

Let’s look at some more examples using other LLMs.

Example 2: Question Answers

We can use the API for the Roberta-base model which can be a source to refer to and reply to. Let’s change the payload to provide some information about myself and ask the model to answer questions based on that.

API_URL = 'https://api-inference.huggingface.co/models/deepset/roberta-base-squad2'
headers = {'Authorization': 'Entertheaccesskeyhere'}

def query(payload):
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.json()
params = {'max_length': 200, 'top_k': 10, 'temperature': 2.5}
output = query({
    'inputs': {
            "question": "What's my profession?",
            "context": "My name is Suvojit and I am a Senior Data Scientist"
    'parameters': params


The code prints the below output correctly to the question – What is my profession?:

{'answer': 'Senior Data Scientist',
 'end': 51,
 'score': 0.7751647233963013,
 'start': 30}

Example 3: Summarization

We can summarize using Large Language Models. Let’s summarize a long text describing large language models using the Bart Large CNN model. We modified the API URL and added the input text below:

API_URL = "https://api-inference.huggingface.co/models/facebook/bart-large-cnn"
headers = {'Authorization': 'Entertheaccesskeyhere'}

def query(payload):
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.json()
params = {'do_sample': False}

full_text = '''AI applications are summarizing articles, writing stories and 
engaging in long conversations — and large language models are doing 
the heavy lifting.

A large language model, or LLM, is a deep learning model that can 
understand, learn, summarize, translate, predict, and generate text and other 
content based on knowledge gained from massive datasets.

Large language models - successful applications of 
transformer models. They aren’t just for teaching AIs human languages, 
but for understanding proteins, writing software code, and much, much more.

In addition to accelerating natural language processing applications — 
like translation, chatbots, and AI assistants — large language models are 
used in healthcare, software development, and use cases in many other fields.'''

output = query({
    'inputs': full_text,
    'parameters': params


The output will print the summarized text about LLMs:

[{'summary_text': 'Large language models - most successful '
                  'applications of transformer models. They aren’t just for '
                  'teaching AIs human languages, but for understanding '
                  'proteins, writing software code, and much, much more. They '
                  'are used in healthcare, software development and use cases '
                  'in many other fields.'}]

These were some of the examples of using Hugging Face API for common large language models.

Future Implications of LLMs

Certainly! Here’s a revised version:

In recent years, there has been a specific interest in Large Language Models (LLMs) such as GPT-3 and chatbots like ChatGPT, which can generate natural language text indistinguishable from that written by humans. While LLMs have made significant breakthroughs in the field of artificial intelligence (AI), concerns have emerged regarding their potential impact on job markets, communication, and society.

A significant worry about LLMs is their potential to disrupt job markets. Over time, Large Language Models may replace humans in tasks such as drafting legal documents, generating customer support chat responses, or writing news blogs. This raises concerns about job losses for roles that can be easily automated.

It is crucial to emphasize that LLMs are not intended to replace human workers but rather serve as tools to enhance productivity and efficiency. While certain tasks may become automated, the increased efficiency and productivity enabled by LLMs could lead to the creation of new jobs. Businesses may, for instance, develop new products or services that were previously too time-consuming or expensive to produce.

LLMs have the potential to impact society in various ways. They could be used to create personalized education or healthcare plans, leading to improved patient and student outcomes. Additionally, LLMs can assist businesses and governments in making better decisions by analyzing large datasets and generating valuable insights.


Large Language Models (LLMs) have brought about a revolution in natural language processing, advancing text generation and understanding. They can learn from extensive datasets, comprehend context and entities, and respond to user queries. LLMs serve as viable alternatives for regular usage across various tasks and industries. However, ethical concerns and potential biases associated with these models need to be addressed. A critical approach is essential to evaluate the societal impact of LLMs. With careful use and continued development, LLMs have the potential to bring positive changes in various domains, but awareness of their limitations and ethical implications is crucial.

Key Takeaways:

  • LLMs possess the ability to understand complex sentences, discern relationships between entities and user intent, and generate coherent and grammatically correct text.
  • The article delves into the architecture of some LLMs, including embedding, feedforward, recurrent, and attention layers.
  • Popular LLMs such as BERT, Bloom, and GPT-3 are discussed, along with the availability of open-source LLMs.
  • Hugging Face APIs can assist users in generating text using LLMs like Bart-large-CNN, Roberta, Bloom, and Bart-large-CNN.
  • The article anticipates that LLMs will revolutionize specific domains in the job market, communication, and society in the future.

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