The introduction to Large Language Models: the core technical component behind systems like Chat GPT, Claude, and Bard. What they are, where they are headed, comparisons and analogies to present-day operating systems, and some of the security-related challenges of this new computing paradigm
This document introduces Large Language Models (LLMs), explaining their training process, which involves compressing vast amounts of text data using GPUs. It covers the stages from pretraining with large datasets to finetuning with specific data for tasks like answering questions. The text also touches on the challenges of understanding LLMs' inner workings, scaling laws, and potential future advancements. Additionally, it addresses security concerns like jailbreaks, prompt injection attacks, and data poisoning, emphasizing the evolving nature of LLM security.
Chat GPT Architecture
Training Process Training an LLM involves two main stages:
- Pretraining: Downloading ~10TB of text and using around 6,000 GPUs over 12 days to create a base model. This stage costs approximately $2M.
- Finetuning: Using specific datasets and collecting high-quality Q&A responses to refine the model into a specialized assistant.
Challenges and Insights Despite their impressive capabilities, the internal workings of LLMs remain largely inscrutable, with billions of parameters collaborating in complex ways that we don't fully understand. This results in occasional peculiarities, like inconsistencies in answering related questions.
Applications and Future Potential LLMs can be customized for specific tasks, integrated into various applications, and scaled up for even greater intelligence. The future promises more advanced capabilities, such as better vision and audio processing, and even the ability to think more deliberately through a "System 2" approach.
Security Concerns As powerful as LLMs are, they come with significant security risks. These include prompt injection attacks, data poisoning, and adversarial inputs. Addressing these vulnerabilities is crucial as LLM technology evolves.
Conclusion LLMs represent a significant leap in AI, with transformative potential across numerous fields. However, understanding their intricacies and addressing security challenges will be key to unlocking their full potential.
References
https://drive.google.com/file/d/1pxx_ZI7O-Nwl7ZLNk5hI3WzAsTLwvNU7/view
https://youtu.be/bSvTVREwSNw
Large language models-- or LLMs --are a type of generative pretrained transformer (GPT) that can create human-like text and code. There's a lot of talk about GPTs and LLMs lately, but they've actually been around for years! In this video, Martin Keen briefly explains what a LLM is, how they relate to foundation models, and then covers how they work and how they can be used to address various business problems. Stay tuned as we explore the fascinating developments in LLMs and their impact on our world!