Language learning models (LLMs) are evolving rapidly, reshaping AI in various industries. In this article, we’ll go over five LLMs that are currently making an impact with their advanced features and wide-ranging use cases.
LLM Basics
Before looking at each model, let’s go over some important LLM concepts that you should be familiar with:
Parameter Count: Parameters are the building blocks of machine learning models, and you can adjust them during training to improve predictions.
The number of parameters tells us how complex and capable the model is. LLMs with more parameters (from 70 billion to over 1 trillion) are better at understanding context, generating detailed text, and handling complex tasks. But larger models need more computational power to run.
Training Data: The success of an LLM depends on the quality and how up-to-date its training data is. These models are trained on huge amounts of data from books, websites, and many other sources. If the data is outdated, models may give older information.
Newer techniques, like Retrieval-Augmented Generation (RAG), help by pulling in real-time data. We’ll discuss more details about each model’s data and how RAG improves them below.
Applications: LLMs are used for many tasks, like content creation, answering questions, coding help, and giving personalized recommendations.
Some models are better for specific tasks—for instance, some excel at creative writing, while others handle technical work more effectively. We will explore how each model performs in different areas.
What to Consider When Choosing an LLM
When you’re deciding which LLM to use, keep these key factors in mind:
Parameter Size vs. Power Needs: You need to balance the number of parameters with the power needed to run the model. A model with too many parameters may require expensive hardware and more energy, while a smaller one might not perform well enough.
Fine-Tuning: To get the best results, you may need to fine-tune the model by training it on your own data or adjusting how it responds. For example, if you want it to handle customer support, you could fine-tune it using a set of frequently asked questions relevant to your business.
Accuracy: You can measure a model’s accuracy through testing, benchmarks, or comparing it against standard metrics. It is important to check how well the model has been tested on tasks similar to yours to understand its strengths and weaknesses.
Cost Efficiency: Think about the cost of training and using the model, including the hardware and operational costs.
Ethics and Safety: Check if the model includes protections against harmful or biased outputs, which is becoming more important in AI development.
Overview of Popular LLMs
Now it’s time to dive into the LLMs that I think are making the biggest impact right now:
GPT-4
OpenAI's GPT-4 is still one of the most powerful models available. It's known for its creativity and accuracy in many different applications. With over a trillion parameters, GPT-4 is great at natural conversations, answering complex questions, and generating creative content.
Many businesses use it for customer support, automation, and content creation, while developers use it for coding help. But its context window is smaller compared to newer models, maxing out at 32k tokens.
Details:
Size: Over 1 trillion parameters
Training Data: 45 terabytes of quality text (up to 2023)
Accuracy: Over 90% in conversation tests
Learning Speed: Fast adaptation
Applications: Used in customer support, automation, content creation, and coding assistance
Training Data Consideration: GPT-4's data goes up to 2023, so it might miss the latest information. Adding real-time data retrieval (RAG) can help it stay up-to-date.
Things to Consider:
Parameter Size vs. Power Needs: It needs a lot of power due to its size.
Fine-Tuning: Can be easily adjusted for different tasks.
Accuracy: Very accurate in conversations.
Cost Efficiency: Expensive to run because of its size.
Ethics: Includes safety measures but is still being improved.
Gemini
Created by Google DeepMind, Gemini is impressive for its speed and efficiency. It’s great for demanding tasks because it learns fast, which helps it adapt to different situations quickly.
Gemini can work with different kinds of data—text, images, and more—making it ideal for creative projects and solving complex problems.
Details:
Size: 500 billion parameters
Training Data: 30 terabytes, including text, images, and structured data (up to 2024)
Learning Speed: 40% faster than similar models
Applications: Best for creative projects and complex problem-solving.
Training Data Consideration: Gemini’s data is current up to 2024, but real-time data retrieval (RAG) can help keep it updated.
Things to Consider:
Parameter Size vs. Power Needs: Requires a lot of power, but slightly less than GPT-4.
Fine-Tuning: Very flexible for different tasks.
Accuracy: Highly accurate, though it varies by task.
Cost Efficiency: Offers good performance at a reasonable cost.
Ethics: Focused on responsible use, but ongoing updates are needed.
LLaMA
Meta’s LLaMA is all about being efficient and adaptable. Even with fewer parameters, it’s highly customizable, letting businesses fine-tune it for specific tasks. It also saves on costs, making it a popular choice for those who want strong AI capabilities without the big expense.
LLaMA is available for free for research and commercial use, but there are limits—services with over 700 million users need a special license, and it can’t be used to train other language models
Details:
Size: 70 billion parameters
Training Data: Extensive but not specific about date range
Cost: 30% cheaper than similar models
Customization: Can be adapted in 500+ ways
Applications: Popular for businesses seeking cost-effective AI
Training Data Consideration: LLaMA’s data covers many topics, but the date range isn’t clear. Adding real-time data retrieval (RAG) can improve its accuracy with current information.
Things to Consider:
Parameter Size vs. Power Needs: Less demanding, so it works in many settings.
Fine-Tuning: Very customizable for specific needs.
Accuracy: Good across different tasks, but accuracy varies.
Cost Efficiency: Very affordable.
Ethics: Ethical measures are included, but there’s room for improvement.
Falcon
Developed by the Technology Innovation Institute, Falcon aims to make AI more accessible. It performs well without needing massive computing resources, which makes it a good choice for smaller businesses.
Falcon is also affordable and doesn't compromise on quality, plus it focuses on energy efficiency.
Details:
Size: 180 billion parameters
Training Data: 20 terabytes (specific date range not mentioned)
Accessibility: Popular with small to medium businesses
Energy Use: Among the top three for low energy consumption
Applications: Great for smaller businesses that need efficient AI solutions
Training Data Consideration: Falcon has a lot of training data, but the exact dates are unclear, which could lead to gaps in knowledge. Using real-time data retrieval (RAG) can help fill these gaps.
Things to Consider:
Parameter Size vs. Power Needs: Balances good performance with efficient power use.
Fine-Tuning: Can be adjusted for different uses.
Accuracy: Generally accurate, but should be tested for specific tasks.
Cost Efficiency: Energy-efficient and affordable for small businesses.
Ethics: Committed to ethical AI, but needs regular updates.
Claude
Anthropic’s Claude is focused on safety and ethics. It’s built to generate helpful and safe responses, making it ideal for companies that care about ethical AI use.
Its expanded context window—now able to handle up to 100k tokens, or about 75,000 words—means it can analyze large documents, which is a major advantage.
With fewer biased outputs and strong safety features, Claude is a solid choice for businesses prioritizing responsible AI.
Details:
Size: 120 billion parameters
Bias Control: 65% fewer biased responses than similar models
Safety: Follows ethical guidelines 85% of the time
Context Window: Expanded from 9,000 to 100,000 tokens
Applications: Ideal for companies that prioritize responsible AI
Training Data Consideration: Claude’s training data is wide-ranging, but its ethical guidelines depend on the quality of that data. Using RAG techniques can help ensure it stays relevant.
Things to Consider:
Parameter Size vs. Power Needs: Moderately demanding, which supports various applications.
Fine-Tuning: Can be customized for ethical purposes.
Accuracy: Measured by how well it follows ethical guidelines.
Cost Efficiency: Reasonably priced.
Ethics: Focuses on reducing bias and ensuring safe outputs, prioritizing responsible AI use. Regular updates and user feedback help maintain its ethical standards.
Conclusion
Each of these LLMs has its own unique strengths. No matter if you need something powerful like GPT-4 or a model that focuses on ethical standards like Claude, there is an option to fit your needs.
As AI continues to grow, it’s all about finding the model that best suits your goals, considering efficiency, safety, cost, and specific requirements. These models are not only leading in technology but also shaping how we use AI in our daily lives.