Large language models (LLMs) are artificial intelligence programs that can understand and generate human-like text. They’re trained on vast amounts of written data from the internet and other sources. This training allows them to perform a wide range of tasks, from answering questions and writing essays to coding and analyzing data.
LLMs have become popular for several reasons:
- Improved technology: Advances in computing power and machine learning techniques have made it possible to create more capable AI models.
- Accessibility: Many LLMs are available through easy-to-use interfaces, making AI technology accessible to more people.
- Versatility: These models can handle a wide variety of tasks, making them useful in many fields.
- Efficiency: LLMs can often complete tasks faster than humans, saving time and resources.
- Continuous improvement: Researchers and companies are constantly working to make these models more accurate and useful.
This article will explore eight of the most popular LLMs available today. We’ll look at who developed each model and why, as well as what makes each one unique. We’ll discuss their strengths and how they compare to each other. This information will help you understand the current state of LLM technology and how these different models might be useful in various situations.
The best LLMs available to the public
1. GPT-4 and GPT-3.5 (OpenAI)
Developed by OpenAI to advance AI capabilities and explore safe and beneficial AI. These models are known for their versatility and strong performance across a wide range of tasks.
GPT-3.5 powers ChatGPT for both free and paid users, excelling in natural language tasks. GPT-4, available only to paid subscribers, offers enhanced capabilities with a larger context window (the amount of text the model can consider at once) and more efficient processing of complex conversations. GPT-4 also supports multimodal inputs in some versions, allowing users to query both text and images. Both models are highly capable in areas like coding, creative writing, and analytical tasks, with GPT-4 generally outperforming GPT-3.5.
2. Claude (Anthropic)
Created by Anthropic with a focus on safety and ethical AI development. Claude aims to be helpful, honest, and harmless.
Claude models are built using Constitutional AI, a technique that explicitly trains the model to adhere to ethical guidelines. This approach makes Claude particularly strong in offering contextually appropriate and safer responses, especially in tasks where avoiding potential harm is critical. Claude excels in natural language understanding, analysis, and complex reasoning. The latest version, Claude 3, features an expanded context window of 200,000 tokens, allowing it to handle extensive tasks and large data inputs. This large context window enables Claude to maintain coherence and relevance across long conversations or when analyzing large documents.
3. PaLM and Gemini (Google)
Developed by Google to push the boundaries of language understanding and generation. These models showcase Google’s AI research capabilities.
PaLM 2, the successor to the original PaLM, comes in multiple sizes (Gecko, Otter, Bison, Unicorn) to suit various applications. It demonstrates strong performance in reasoning, code generation, and multilingual understanding. Gemini, Google’s latest model, is designed to be multimodal, excelling in tasks that combine text, image, and potentially other forms of data. While Gemini is still in early development, it represents Google’s push towards more integrated and versatile AI models.
4. LLAMA and LLAMA 2 (Meta)
Created by Meta (formerly Facebook) as open-source alternatives to proprietary models, promoting AI research and development.
LLAMA models, especially LLAMA 2, are known for their efficiency and strong performance despite smaller model sizes compared to some competitors. They’re particularly useful for researchers and developers looking to fine-tune models for specific applications. Being open-source, these models allow for greater transparency and customization. LLAMA 2 improved upon the original LLAMA with better performance and a more permissive license for commercial use.
5. Titan (Amazon)
Developed by Amazon to power their AI services and compete in the AI market.
Titan is designed to be versatile for various AWS (Amazon Web Services) applications. While Amazon has not disclosed extensive technical details about the model, it’s known to be particularly strong in tasks related to e-commerce, content generation, and summarization. Its primary use is within AWS for AI-driven services like personalization and recommendation systems.
6. Falcon (Technology Innovation Institute)
Created by the Technology Innovation Institute in Abu Dhabi to contribute to global AI research and showcase Middle Eastern AI capabilities.
Falcon models, particularly the Falcon 180B (180 billion parameters), are known for their strong performance relative to their size. They excel in various natural language processing tasks, often outperforming larger models. Notably, Falcon models are open-source, making them accessible for research and development purposes.
7. BLOOM (BigScience)
Developed as a collaborative, open-science project to create a multilingual, open-source language model.
BLOOM is particularly strong in multilingual tasks, supporting 46 languages and 13 programming languages. It was developed as part of the BigScience project, emphasizing transparency and accessibility, especially for non-English languages and marginalized communities. This focus on inclusivity makes BLOOM particularly important in efforts to democratize AI research and applications globally.
8. Jurassic-1 and J2 (AI21 Labs)
Created by AI21 Labs to offer alternative AI models with unique capabilities.
These models are known for their strong performance in specific areas like paraphrasing and text manipulation. J2, in particular, is designed to have improved factual accuracy and reduced hallucination (generating false or unsupported information) compared to some other models. This focus on reliability makes J2 particularly strong in tasks where factual accuracy is crucial.
Looking ahead
Large language models have significantly changed how we interact with artificial intelligence. They’ve made AI more accessible and useful for everyday tasks, from writing assistance to complex problem-solving. However, it’s important to remember that these models have limitations. They can make mistakes, and they require careful use to avoid potential issues like bias or misinformation.
As LLM technology continues to advance, we can expect to see even more powerful and specialized models. These developments will likely bring new opportunities and challenges and you’ll need to continue assessing which model suits your requirements best.
While this article provides an overview of some popular models as of 2024, new developments are happening regularly. For the most up-to-date information, it’s always best to check the official sources or recent publications from AI research institutions.