Hello Learners…
Welcome to the blog…
Topic: Demystifying LLMs: Unlocking the Concepts of Generative AI
Table Of Contents
- Introduction
- What is Generative AI?
- Demystifying LLMs: Unlocking the Concepts of Generative AI
- Summary
- References
Introduction
This post discusses Demystifying LLMs: Unlocking the Concepts of Generative AI. generative AI is becoming increasingly popular so we have to know the concepts behind it and learn how it is works and implemented.
What is Generative AI?
Generative AI refers to a branch of artificial intelligence that focuses on creating models and algorithms capable of generating new content or data that resembles a specific input or follows a certain pattern. Unlike traditional AI models that are mainly used for prediction or classification tasks, generative AI aims to generate new and original content.
It involves designing and manipulating the input text given to the model to achieve desired results.
By carefully constructing prompts, researchers and developers can control the behavior, style, or content of the generated output.
Demystifying LLMs: Unlocking the Concepts of Generative AI
Many businesses are adopting OpenAI’s API and Models to create chatbots. As developers, leveraging LLMs as a tool allows us to build software applications rapidly, including chatbots, by making API calls to LLMs.
Generative AI has seen remarkable advancements in various domains, including computer vision, natural language processing, and art generation.
Understanding the capabilities of LLM APIs enables developers to develop applications quickly and efficiently.
How Large Language Models Work?
As we know about text generation, In the text generation we give a prompt like
- I love eating…
And ask an LLM to fill in what things are likely completed given this prompt
It generates like
- I love eating out with friends
- I love eating with my family
- I love eating Burger
- I love eating Pizza
But, how can a model learn to do this?
The main tool used to train LLM is actually supervising learning as we know that in supervised learning, a computer learns an input-output or X and Y mapping using label training data.
Let’s take an example if we are using supervisor learning to learn to classify the sentiment or restaurant reviews we might collect a training set, where reviews like,
- Pizza is good – positive
- The burger is not so good – negative
- Service was slow – negative
- Service was fast – positive
So, the process for supervised learning is to get label data and then train a model on data, and after training, we can deploy it and call the model and give it a new restaurant review like,
- Input – best pizza I have ever had
- Output – positive
It turns out that supervised learning is a core building block for training large language models
In the development of large language models (LLMs), there are precisely two types of large language models(LLMs).
- Base LLMs
- Instruction Tuned LLMs
1. Base LLMs
Base language models (LLMs) have undergone training to predict the next word based on extensive text training data, often sourced from the internet and various other reliable sources. These LLMs serve as the foundation for language understanding and generation.
A base LLM refers to a large language model that hasn’t been fine-tuned for any specific task. These models are trained on vast datasets comprising text and code, allowing them to exhibit versatility in performing various tasks.
- Generating text
- Translating languages
- Answering questions
- Summarizing text
- Writing different kinds of creative content:
Text Generation Examples,
Example:1
- Input: once upon a time there was a boy….
- the answer generated by the base LLMs is…
- Output: he lives in a magical village with his best friends
Example:2
Input: what is the capital of India?
Outputs can be anyone:
- what is India’s largest city?
- what is India’s population?
- what is the currency of India?
benefits of using base LLMs
- Versatility: used for a variety of tasks.
- Ease of use: easy to use and does not require any special training.
- Cost-effectiveness: less expensive than fine-tuned LLMs.
drawbacks of using base LLMs:
- Accuracy: not always as accurate as fine-tuned LLMs.
- Specificity: sometimes may not be able to generate text that is very specific or accurate.
- Bias: It is important to acknowledge that instruction-tuned LLMs may exhibit biases based on the dataset used for their training.
2. Instruction Tuned LLMs
Instruction-tuned LLMs offer a remarkable advancement in the realm of large language models (LLMs). Unlike their base counterparts, instruction-tuned LLMs are fine-tuned specifically on a range of tasks presented as instructions. This fine-tuning process, known as instruction tuning, enables LLMs to learn and strive to follow instructions accurately.
Some Examples:
Input: what is the capital of India?
the answer to the instruction tuned LLMs is as below
Output: the capital of India is New Delhi.
which can be useful for a variety of tasks, such as,
- Generating creative text content
- Answering questions
- Translating languages
How We Can Go From Base LLM To Instruction-Tuned LLM?
The process of training instruction-tuned language models (LLMs) involves a two-step approach. Initially, base LLMs are trained on extensive text data, providing them with a strong foundation of language understanding.
The next step involves fine-tuning these base LLMs by exposing them to inputs and outputs in the form of instructions. The LLMs attempt to follow these instructions to the best of their abilities. This training approach helps the models learn how to be helpful, honest, and harmless in their responses.
To further enhance their performance, a technique called Reinforcement Learning from Human Feedback (RLHF) is often applied. RLHF refines the models by incorporating feedback from human evaluators, allowing them to better understand and adhere to instructions provided by users.
By going through this iterative training process, instruction-tuned LLMs are designed to provide valuable assistance while maintaining honesty and safety.
While base language models (LLMs) can be found on the internet, it is generally recommended to prioritize the use of instruction-tuned LLMs for practical applications. Instruction-tuned LLMs are easier to work with and have gained popularity due to advancements made by OpenAI and other LLM companies in terms of safety and alignment.
Exactly! When working with instruction-tuned LLMs, we can think of it as instructing another person who is intelligent but lacks specific knowledge about our task. These models are trained to understand and follow instructions provided to them, just like a person would.
Sometimes, when an LLM doesn’t work, at that time the instructions are not clear enough for the LLM models to understand.
benefits of using instruction-tuned LLMs
Improved performance:
Whether it’s generating creative text content, answering questions, or translating languages, instruction tuning enhances LLMs’ capabilities and boosts their output quality.
Increased flexibility:
By training them to follow instructions, LLMs become versatile tools that can be repurposed for a wide range of tasks
Reduced development time:
Training LLMs on instruction datasets require a significantly smaller amount of data compared to traditional task-specific examples.
Summary
If we summarise the above post then In the world of generative AI, prompt engineering holds the key to unlocking ChatGPT’s creative potential. By understanding the techniques of prompt engineering, developers and researchers can harness the full range of ChatGPT’s capabilities, shaping its responses to align with specific objectives