[1hr Talk] Intro to Large Language Models

Summary

The video provides an introduction to large language models like GPT and CLA series. It explains how they work, how they are trained on massive datasets, and their promising capabilities. It also discusses emerging challenges like security vulnerabilities from jailbreak, prompt injection, and backdoor attacks. Overall, the talk highlights the transformative potential and risks of this powerful new computing paradigm centered on large language models.

Takeaways

• 📊 LLM performance scales predictably with model size and training data
• 🧠 LLMs currently only have 'system 1' thinking, lacking deeper reasoning
• 🔭 Self-improvement through reinforcement learning may enable LLMs to exceed human capabilities
• 🛠 LLMs are gaining multimodal abilities like image generation and speech
• 🤝 Fine-tuning with human feedback is key for alignment and task performance
• 🌎 Pre-training compresses broad knowledge, while fine-tuning targets specific skills
• 🎛 LLMs are becoming like operating systems that orchestrate tools for problem-solving
• 🔒 'Jailbreak' attacks can bypass LLM safety measures through deception
• 📝 'Prompt injection' can hijack LLMs by inserting malicious instructions
• 🥷 Data poisoning during training can implant hidden LLM vulnerabilities

Q & A

What are the two main files that make up a large language model like LLAMa 270B?

The two main files are the parameters file, which stores the weights of the neural network, and the run file, which contains code to run the neural network using those parameters.

How much would it cost to train a model like LLAMa 270B from scratch today?

Training a model like LLAMa 270B from scratch today would cost around $2 million for 6,000 GPUs running for 12 days.

What is the difference between pre-training and fine-tuning for large language models?

Pre-training trains the model on a large quantity of text from the internet to build general knowledge. Fine-tuning trains the model on a smaller set of high quality human-labeled question-answer pairs to adapt it to be an assistant.

What tools can large language models leverage to enhance their capabilities?

Large language models can leverage tools like web browsing, calculators, Python code execution, and image/audio generation to enhance their capabilities beyond just text.

How do the scaling laws enable improvements in large language models?

The scaling laws show that model performance reliably improves with more parameters and training data. So bigger models trained on more data get better automatically.

What is an example of a 'jailbreak' attack on large language models?

Tricking the model into a roleplay scenario that circumvents its safety constraints, like pretending to be a helpful relative providing dangerous advice.

What is retrieval augmented generation in large language models?

A capability where the model can reference and 'browse' through text files provided by the user as additional context when generating responses.

What is the difference between system 1 and system 2 thinking?

System 1 is fast, instinctive thinking while system 2 is slower, more conscious thinking involving reasoning and analysis. Current LMs only exhibit system 1 capabilities.

How was AlphaGo able to surpass human abilities at Go?

By using self-play reinforcement learning to improve itself through millions of games, rather than just imitating human games.

What are some ways large language models could be customized for specific tasks?

Providing custom training data, instructions, knowledge sources, and fine-tuning capabilities to adapt models to particular domains.