Large Language Models: Creation, Operation, Application
Large Language Models (LLMs) are not just another technology trend, but powerful tools that have the potential to change the way we interact with information and technology. They are the basis of chatbots, search engines, content creation tools, and much more.
What are these models, how do they work and who develops them? Let’s figure it out together with the Dexola team.
How do LLMs work?
LLMs are based on neural networks that mimic the functioning of the human brain. These networks are made up of many interconnected nodes (neurons) that process information. LLMs are trained on massive amounts of text data, allowing them to understand patterns in natural language, predict the next words in a sentence, and generate text that resembles human writing.
The learning process can be divided into several stages:
- Data collection. LLMs are trained on huge amounts of text data from various sources such as books, articles, websites, and social media.
- Tokenization. Text is broken down into smaller units called tokens. These can be words, parts of words, or even individual characters.
- Embedding. Each token is assigned a vector representation that reflects its meaning and context.
- Model training. The model is trained to predict the next token in the sequence based on the previous tokens. This process is repeated millions of times and the model gradually improves its predictions.
- Text generation. Once trained, the model can generate text that is similar to the text it was trained on, but can also generate new ideas and stories.
It is worth considering that to get the desired result for each LLM site, you need to look for your approach when writing a request called prompt. This is because different platforms such as Chat GPT or Claude use different data structures, information formats, and request-processing autonomous AI algorithms.
Autonomous AI agents are transforming technology by independently performing tasks and making final decisions. Unlike traditional AI solutions, they analyze, plan, adapt, and learn from experience. Advances in machine learning and natural language processing have expanded their use in personal assistants, chatbots, management systems, and autonomous vehicles, showcasing their potential in various fields.
Key Players in the LLM Market
Developing an LLM requires enormous computing resources and artificial intelligence expertise. Therefore, the market is dominated by large technology companies and research laboratories.
| Model | ChatGPT 4.0 | Llama 3.1 | Claude 3.5 Sonnet | Gemini |
| Developer | OpenAI | Meta AI | Anthropic | Google AI |
| Price | $20/month (ChatGPT Plus) | Open Source | $20/month (Claude Pro) | $19.99/month (Google One AI Premium), $30/month (Gemini Business) |
| Strengths | Creative writing, code generation, reasoning | Scalability, performance, openness | Reasoning, coding, safety | Multimodality, music & video generation, context window |
| Weaknesses | Cost, limited access | Less training data than ChatGPT | Less usage experience than ChatGPT | Unknown |
| Context Window | 8K/32K tokens | Version dependent | 100K tokens | 2 million tokens |
| Unique Features | Wide integration, constant updates | Active developer community | Strong safety controls | Multimodality, built-in assistants |
| Use cases | Chatbots, content generation, translation | Research, commercial use | Chatbots, business applications | Search, chatbots, content generation |
ChatGPT
Developer. OpenAI — The company, founded by Elon Musk and Sam Altman, is a pioneer in the LLM field. OpenAI developed GPT (Generative Pre-trained Transformer), a family of models that revolutionized the field of natural language processing. ChatGPT, based on GPT, has become one of the most popular chatbots in the world.
Price. ChatGPT Plus — $20/month.
Strengths:
- Excels in creative writing tasks, such as composing poems, scripts, and musical pieces.
- Proficient in code generation, offering assistance in various programming languages and debugging.
- Demonstrates strong reasoning abilities, capable of solving complex problems and analyzing information.
Weaknesses:
- Requires a paid subscription for full access, limiting availability for some users.
- Occasionally generates incorrect or nonsensical information, requiring fact-checking.
- Can be lengthy and overly cautious in responses, sometimes missing the desired brevity.
Context Window. 8K/32K tokens (depending on the subscription plan).
Unique Features. Wide integration with various platforms and applications. Continuous updates and improvements based on user feedback and research. Offers a user-friendly interface for easy interaction. Music and 1080p video generation, 2 million token context window, multimodality, text-to-image integration, reduced pricing, built-in assistants, new generation of TPUs and machine learning processors are expected.
Use Cases. Widely used for chatbots, content generation (articles, blog posts, marketing copy), translation, and other creative and informative tasks.
Developer. Meta AI — Facebook is also actively working on LLMs. Their models, such as BlenderBot and RoBERTa, are used to improve the quality of machine translation, create chatbots, and other tasks.
Price. Open Source.
Model Size. 405 billion parameters.
Strengths:
- Highly scalable and performant, capable of handling large-scale tasks efficiently.
- Open-source nature allows for greater flexibility and customization by developers.
- An active community of contributors and researchers constantly improving the model.
Weaknesses:
- Requires technical expertise and computational resources to set up and run effectively.
- Trained on less data than some other models, potentially impacting performance on certain tasks.
- May require additional fine-tuning for specific use cases.
Context Window. Version dependent (can be adjusted based on hardware and requirements).
Unique Features. Offers various model sizes (7B, 13B, 30B, 65B, 70B, 405B) to cater to different needs and resources. Focus on research and commercial applications, with potential for wide adoption.
Use cases. Primarily used for research purposes, but also finding applications in commercial settings such as content generation, translation, and data analysis.
Claude
Developer. Anthropic — the company, founded by former OpenAI employees, develops LLMs that are highly secure and ethical. Their Claude model is marketed as being safer and less likely to generate harmful or offensive content than other models.
Price. Claude Pro — $20/month.
Strengths:
- Prioritizes safety and ethical considerations in its design and training.
- Demonstrates strong reasoning and coding abilities, making it suitable for technical tasks.
- Shows promise in generating coherent and informative responses.
Weaknesses:
- Limited access through API, requiring approval and potentially incurring costs.
- Relatively less usage experience compared to more established models.
- Still under development, with the potential for further improvements and refinements.
Context Window. 100K tokens.
Unique Features. Robust safety measures to minimize harmful or biased outputs. Focus on research and business applications with a strong ethical foundation.
Use cases. Targeted towards chatbots, customer service, and various business applications where safety and ethical considerations are paramount.
Developer. Google AI — actively invests in research and development in the field of AI. Their latest model, the Gemini, promises to be even more powerful and versatile than previous models. Gemini will be able to generate not only text, but also music, video, and other types of content.
Price. Google One AI Premium — $19.99/month, Gemini Business — $30/month.
Strengths:
- Expected to have multimodal capabilities, processing text, images, and potentially other data types.
- Anticipated to have a large context window, enabling it to handle extensive conversations and documents.
- Built-in assistant features for enhanced user interactions.
Weaknesses:
- Not yet available to the general public, limiting access and real-world testing.
- Pricing details for API and Google Cloud access are yet to be determined.
- Specific performance and capabilities are still unknown.
Context Window. 2 million tokens.
Unique Features. Multimodal capabilities open up possibilities for creative applications and enhanced user experiences. A large context window could revolutionize information processing and long-form content generation.
Use cases. Potential applications include search, chatbots, content generation, and various tasks requiring multimodal understanding and large context windows.
LLMs have the potential to change many aspects of our lives. They can improve the quality of machine translation, create smarter chatbots, help with coding, content creation, and even scientific research. LLMs can also be used to address social issues such as combating misinformation and making educational resources more accessible.
Le Chat
Developer. Mistral AI — a French startup founded by former DeepMind and Meta AI researchers, focusing on developing efficient and powerful language models.
Price. Varies depending on the model and usage. Offers both open-source and commercial models.
Strengths:
- Highly efficient models that can run on less powerful hardware.
- Strong multilingual capabilities, supporting a wide range of languages.
- Offers both open-source and proprietary models for different needs.
- Customizable and adaptable for specific use cases.
Weaknesses:
- Relatively new to the market compared to more established players.
- Less widespread adoption and integration with existing tools and platforms.
- Limited track record in large-scale commercial applications.
Context Window. Up to 32K tokens for their latest models.
Unique Features. Utilizes a Mixture of Experts architecture for more efficient training and inference. Focuses on creating models that balance performance and resource requirements.
Use cases. Suitable for various natural language processing tasks, content generation, code completion, and customized language model applications.
LLMs have the potential to change many aspects of our lives. They can improve machine translation quality, create smarter chatbots, help with coding, content creation, and even scientific research. LLMs can also be used to address social issues such as combating misinformation and making educational resources more accessible.
Dexola and Large Language Models
LLM is a rapidly growing field and we can expect even more impressive results soon. As technology advances and computing power increases, LLMs will become even more powerful, versatile, and accessible.
Dexola, as a blockchain and artificial intelligence company, is at the forefront of research and development in the field of LLM. We see great potential in using LLM to create innovative products and services that can change the world for the better.
LLMs can be used to improve our existing services, such as:
- Smart contract development. LLMs can help automate the creation and analysis of smart contracts, making them more accessible and secure.
- Blockchain data analysis. LLMs can be used to analyze large volumes of blockchain data, identify patterns, and gain valuable insights.
- Creation of dApps. LLMs can help create more intuitive and user-friendly interfaces for dApps, as well as automate some development processes.
In addition, we are actively exploring new opportunities that large language models offer:
- Decentralized LLMs. We are exploring the possibility of creating LLMs that run on the blockchain, making them more transparent, secure, and censorship-resistant.
- LLM for Web3. We are developing LLMs that can be used to create new types of Web3 applications, such as decentralized social networks, marketplaces, and games.
Conclusion
Large language models are revolutionary tools that have the potential to change the way we interact with information and technology. They are already being used in various fields, and their potential is enormous. As technology advances, LLMs will become even more powerful and versatile.
Dexola, as a reliable blockchain and AI partner for Web3 startups, is committed to using LLM to create innovative products and shape a future where technology serves society.
