Large Language Models and GPT-4: Architecture and OpenAI API

Author(s): Simranjeet Singh

Originally published on Towards AI.

Introduction

Large language models have revolutionized the field of natural language processing in recent years. These models are trained on massive amounts of text data and can generate human-like language, answer questions, summarize text, and perform many other language-related tasks. One of the most highly anticipated models in this field is the upcoming GPT-4, which is rumored to have a staggering trillion parameters.

In this article, we will explore the impact of large language models on natural language processing and how they are changing the way we interact with machines. U+1F4B0 DONATE/TIP If you like this Article U+1F4B0

Watch Full YouTube video with Python Code Implementation with OpenAI API and Learn about Large Language Models and GPT-4 Architecture and Internal Working.

Impact of GPT-4 on NLP

The sheer scale of GPT-4, if true, would make it the largest language model ever created, and its potential impact on natural language processing is immense. With such a massive model, we can expect unprecedented levels of accuracy and sophistication in language understanding and generation, as well as the ability to process vast amounts of text data quickly and efficiently.

What are Large Language Models?

Large language models are a type of artificial intelligence (AI) model that are specifically designed to understand natural language. They are capable of processing and generating text, and can be used for a wide range of applications, including language translation, summarization, and question-answering. These models are typically composed of a large number of interconnected nodes, which allow them to make predictions about what words are likely to come next in a sentence, based on the context in which they are used.

One of the most well-known large language models is GPT-3, which has 175 billion parameters. In GPT-4, Which is even more powerful than GPT-3 has 1 Trillion Parameters. It’s awesome and scary at the same time. These parameters essentially represent the “knowledge” that the model has acquired during its training. The more parameters a model has, the more accurate its predictions are likely to be, since it has access to a wider range of contextual information.

Large language models are typically trained on massive amounts of text data, which allows them to learn the patterns and relationships between words and phrases.

Real-world examples of large language models include virtual assistants like Siri and Alexa, language translation services like Google Translate, and chatbots used for customer support.

How do Large Language Models Work?

Large language models work by predicting the probability of a sequence of words given a context. To accomplish this, large language models use a technique called self-attention. Self-attention allows the model to understand the context of the input sequence by giving more weight to certain words based on their relevance to the sequence.

Each word in the input sequence is first transformed into a vector representation called an embedding. These vectors represent the meaning of the word in the context of the sequence. The model calculates three types of vectors for each word: the query vector, the key vector, and the value vector. These vectors are used to calculate the attention score for each word in the sequence. The attention score reflects how important each word is to the context of the input sequence.

The attention scores are then used to weigh the value vectors for each word. The weighted value vectors are then summed to produce a context vector, which represents the context of the input sequence. The context vector is then used to predict the probability of the next word in the sequence. The output of the model is a probability distribution over the vocabulary, which can be sampled to generate new text.

Coding Example using OpenAI API

GPT-4 has made it easier to access large language models by providing an API that developers can use to generate natural language text. In this section, we will explore how to use the GPT-4 API for various natural language processing tasks.

Example 1: Generating Python code One useful application of the OpenAI API is generating code based on a given prompt. Let’s say we want to generate Python code that takes in an array of lists and then Finds the Odd and Even in it. We can use the OpenAI API to generate the code for us. Here’s an example:

import openai
openai.api_key = "API_KEY"

def generate_code(text):
 response = openai.Completion.create(
 engine="text-davinci-003",
 prompt=f"Write Python Code for {text}",
 max_tokens=512,
 n=1,
 stop=None,
 temperature=0.7,
 )

 code = response.choices[0].text.strip().split('\n')
 return code

data = generate_code("Find Odd and Even number in Array")
print("\n".join(data))

# --------------------------OUTPUT--------------------------------#

#Create an array
arr = [1, 2, 3, 4, 5]

#Iterate through the array
for num in arr:
 #Check for odd number
 if num % 2 != 0:
 print(f"{num} is an odd number")
 else:
 print(f"{num} is an even number")

Example 2: Sentiment analysis Another application of the OpenAI API is sentiment analysis. Let’s say we want to analyze the sentiment of a given piece of text. We can use the OpenAI API to do this. Here’s an example:

#Sentiment Analysis
import openai
openai.api_key = "API_KEY"

def get_sentiment(text):
 response = openai.Completion.create(
 engine="text-davinci-002",
 prompt=f"Sentiment analysis: {text}\nSentiment:",
 max_tokens=1,
 n=1,
 stop=None,
 temperature=0.7,
 )

 sentiment = response.choices[0].text.strip()
 return sentiment

get_sentiment("You are idiot!")

# --------------------------OUTPUT--------------------------------#

Negative

Example 3: Translation Finally, we can use the OpenAI API for translation. Let’s say we want to translate a piece of text from English to French. We can use the OpenAI API to do this. Here’s an example:

#Language Translation
def translate(text, target_language):
 response = openai.Completion.create(
 engine="text-davinci-002",
 prompt=f"Translate '{text}' to {target_language}:",
 max_tokens=1024,
 n=1,
 stop=None,
 temperature=0.7,
 )

 translation = response.choices[0].text.strip()
 return translation

translate("Hi, This is My Youtube Channel, FreeBirds Crew", "French")

# --------------------------OUTPUT--------------------------------#

'Salut, voici ma chaîne Youtube, FreeBirds Crew.'

Example 4: Building a ChatBot Application using OpenAI API. It can talk with you like a Human. Given perfect prompt to the OpenAI API will give more perfect responses. Here’s an example:

#Chat Application
def chat(prompt, temperature=0.5):
 response = openai.Completion.create(
 engine="text-davinci-003",
 prompt=prompt,
 max_tokens=100,
 n=1,
 stop=None,
 temperature=temperature,
 )

 chat_response = response.choices[0].text.strip()
 return chat_response

while True:
 input_res = input("You")
 print("You: ",input_res)
 result = chat(input_res)
 print("ChatGPT: ", result)

For more Explanation and detail, Check the below video that explain Architecture and Working of Large Language Models in Interactive Way:

Final Thoughts

Large language models such as GPT-4 have revolutionized the field of natural language processing by allowing computers to understand and generate human-like language. These models use self-attention techniques and vector embeddings to produce context vectors that allow for accurate prediction of the next word in a sequence. As these models continue to improve and become even more powerful, we can expect to see even more exciting applications of natural language processing in the future.

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Published via Towards AI