An Introduction to “Base” and “Instruction Tuned” Large Language Models
Exploring the World of LLMs and Their Impact on AI Development
Introduction
Large Language Models (LLMs) have revolutionized the field of artificial intelligence, offering a wide range of applications and opportunities. These advanced AI models have made it possible to build powerful software applications using APIs and are transforming the way we interact with technology. In this blog post, we will provide an overview of LLMs, discuss their types, delve into their history and growth, and explore the opportunities they present for the future.
Types of LLMs
There are two primary types of LLMs:
- Base LLMs
- Instruction tuned LLMs
Related Read on Instruction Tuned LLMs: Top Open-Sourced Large Language Models Revolutionizing Conversational AI
Base LLMs
These models are trained on massive amounts of text data, often from the internet or other sources. Their primary function is to predict the next word in a given context. For example, when prompted with “What is the capital of France?” a base LLM might complete the sentence with “What is the capital of India?”. The GPT-3, Bloom etc. are few examples of such base large language models.
Instruction Tuned LLMs
These models are designed to follow instructions more accurately. They begin with a base LLM and are fine-tuned with input-output pairs that include instructions and attempts to follow those instructions. Reinforcement Learning from Human Feedback (RLHF) is often employed to refine the model further, making it better at being helpful, honest, and harmless. As a result, instruction tuned LLMs are less likely to generate problematic text and are more suitable for practical applications.
As per the previous example, for the prompt “What is the capital of France?” the response of Instruction tuned models would be “Paris” or “Paris is the capital of France”.
Examples of these breeds of LLMs include OpenAI’s ChatGPT and codex, Open Assistant etc. which have been extensively used in various applications, ranging from chatbots to content generation.
LLM Types for Dummies: A Simple Analogy
To better understand the concept of LLMs and their types, let’s use an analogy that compares them to a fresh college graduate.
Base LLMs can be likened to a recent college graduate who has read extensively and accumulated a wealth of knowledge and insights on various topics. Just like this graduate, base LLMs have been trained on massive amounts of text data and can generate relevant responses based on the context they are given. However, they might not always be precise or focused on specific instructions.
Instruction Tuned LLMs, on the other hand, can be compared to the same college graduate who has now decided on a focused career objective, such as becoming a Python Developer at a company. This graduate has gone through additional training to hone their skills in their chosen field and has received guidance from senior professionals to become more proficient at their job. Similarly, instruction tuned LLMs are fine-tuned with input-output pairs that include specific instructions and attempts to follow those instructions. The model is further refined using Reinforcement Learning from Human Feedback (RLHF), making it better equipped to follow instructions accurately and generate more relevant and helpful outputs.
History and Growth of LLMs
The development of LLMs can be traced back to the emergence of deep learning and natural language processing. As these fields progressed, researchers began experimenting with larger models and training data, leading to the creation of more powerful language models.
The growth of LLMs accelerated with the introduction of models like Google’s BERT and OpenAI’s GPT series. These breakthroughs demonstrated the potential of LLMs for various applications, including natural language understanding, translation, and content generation. As LLMs continue to evolve, they are becoming increasingly more efficient, accurate, and versatile.
Opportunities and Future Outlook
LLMs offer a myriad of opportunities for developers and businesses alike. Some of the most promising applications include:
Chatbots and virtual assistants: LLMs can be used to create more intelligent and context-aware chatbots, capable of understanding and responding to user queries effectively. AI-Agents will also be capable of processing and assisting us with much complexity.
Content generation: LLMs have the potential to generate high-quality content for various purposes, such as blog posts, social media updates, or product descriptions.
Sentiment analysis and customer feedback: LLMs can help businesses better understand customer feedback and sentiment, enabling them to make data-driven decisions.
Language translation: LLMs can be employed for advanced language translation tasks, breaking down language barriers and facilitating global communication.
And there could be many more.
Conclusion
As LLM technology continues to advance, the possibilities for creating new and innovative applications are seemingly limitless. By understanding the different types of LLMs and their potential uses, developers can harness the power of these advanced AI models to transform industries and shape the future of technology.
Thanks. In my next article, I will be talking about key principles for how to write prompts to prompt engineer effectively. Make sure to follow me and not miss my future articles. Now that you’ve came this far into the article, I think you might find these articles interesting:
1. Efficiently Save $$$ on OpenAI GPT API Calls: Tips and Techniques
3. A Guide to Prompt Writing for Large Language Models like GPT
