Large language models like Lama 270B contain billions of parameters that are trained on massive datasets to predict the next word in a sequence. Model training requires GPU clusters and takes months, while model inference to generate text is quite fast. Through prompt engineering and fine-tuning, language models can be adapted into helpful assistants. There are exciting future capabilities like multimodality, system 2 thinking, and self-improvement, but also new security challenges to address.
An Intro to Large Language Models and Prompt Engineering for Assistants
🚀 Llama 270B has 70 billion parameters stored in a 140GB file. It also needs a small runtime to execute the neural network architecture.
🧠 Pre-trained models like Llama 270B compress ~10 TB of internet text into a compact "lossy zip file" of knowledge contained in the parameters.
💻 The two model files can run inference locally on a laptop without any internet connectivity. The model can generate surprising coherent text like code, products, or articles.
📊 Obtaining the parameters through model training is far more complex, requiring GPU clusters processing 10s of TB of text over weeks at a cost of millions of dollars.
💡 While next word prediction seems simple, the objective forces the model to learn a lot about how language works, compressing knowledge about the world into the parameters.
🌍 The model remembers facts about what it's trained on, but also hallucinates new combinations of knowledge in its text generations.
🤔 We know the architecture but not how parameters represent knowledge. LMs seem mostly inscrutable, requiring empirical evaluations of their capabilities.
⚙️ Model weaknesses are addressed by additional fine-tuning rounds with more labeled Q&A data to improve responses.
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Two Model Files, But Billions of Parameters: LMs only require a parameters file and runtime code, but the magic that makes them work comes from the billions of optimized neural network parameters representing implicit knowledge.
Pre-Training Compresses Text to Knowledge: Training scrapes and compresses internet text over weeks using GPU clusters costing millions of dollars. The result is a lossy "zip file" of knowledge in the parameters.
Perplexing Knowledge Representation: We know the full neural network architecture but parameters representing knowledge seem mostly inscrutable, requiring empirical capability evaluations.
Fine-Tuning Aligns Model for Assistance: Further training on Q&A data adapts models to assist properly, but requires extensive prompt engineering and iteration on weaknesses.
Multimodal Future: LMs will utilize more perceptual abilities alongside text, like images, audio, video and more for a single model OS experience.
Long-Term Thinking Aspirations: Researchers hope to move beyond instinctive text generation to deliberate thinking with accuracy tied to compute time, more like Alpha Go.
New Security Challenges Emerge: Despite defenses, new attack techniques like jailbreaking prompts, backdoors, and adversarial examples threaten reliable assistance.