State of GPT

🌰 Wisdom in a Nutshell

Essential insights distilled from the video.

1. Four stages of training AI assistants.
2. Difference between base models and assistant models.
3. Reinforcement learning and reward modeling.
4. Leveraging human judgment in language models.
5. Improving transformer performance in NLP.
6. Prompt engineering and tree search algorithms.
7. Retrieval augmented generation and effective prompts.
8. Constraint prompting and fine tuning in LLMs.

🔍 Wisdom Unpacked

Delving deeper into the key ideas.

1. Four stages of training AI assistants.

The process of training AI assistants consists of four stages: pre-training, supervised fine tuning, reward modeling, and reinforcement learning. During pre-training, a large dataset is gathered, and a base model is created, which requires significant computational resources. This base model is then fine-tuned using supervised learning to create an assistant model capable of answering questions.

2. Difference between base models and assistant models.

There is a fundamental difference between base models and assistant models in AI language generation. Base models are not designed to answer questions, while assistant models are trained on top of base models to accomplish this task. Supervised fine-tuning ensures these models perform better than base models in generating responses and understanding the structure of the text.

3. Reinforcement learning and reward modeling.

Reinforcement learning is another key process used in training language models. By collecting high-quality data from human contractors, reward modeling can be used to create a loss function that improves performance. Reinforcement learning then trains the model by reinforcing positive tokens and decreasing the probability of negative ones.

4. Leveraging human judgment in language models.

Leveraging human judgment is crucial in improving AI models. By comparing and ranking multiple completions generated by a model, human input can help train the model more effectively, especially in creative tasks such as generating unique, Pokemon-like creatures.

5. Improving transformer performance in NLP.

Improving transformer performance in natural language processing involves prompting and step-by-step thinking. Transformers need to process information one step at a time, and sampling multiple times can significantly improve success rates. However, it is essential to be aware of the potential for transformers to get stuck in a sequence of tokens.

6. Prompt engineering and tree search algorithms.

Prompt engineering and tree search algorithms can further refine language models. These techniques allow AI to maintain multiple completions for a given prompt, scoring them and keeping the best performing ones. Research is ongoing to find even more effective methods for text generation.

7. Retrieval augmented generation and effective prompts.

Transformers and Retrieval Augmented Models can be used for problem-solving, but they often require specific prompts and instructions. Retrieval augmented generation indexes relevant data for efficient access, and effective prompts, like step-by-step instructions, can enhance performance, leading to successful outcomes.

8. Constraint prompting and fine tuning in LLMs.

Large Language Models (LLMs) can be improved using constraint prompting and fine-tuning. Constraint prompting enforces templates in LLM outputs, while fine-tuning adjusts the model's weights for better performance. These techniques, along with human data contractors and synthetic data pipelines, can overcome limitations and biases in LLMs, making them more suited for low stakes applications with human oversight.