Summary State of GPT | BRK216HFS - YouTube (Youtube) www.youtube.com
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One Line
The text discusses various aspects of GPT models, including prompt engineering, model limitations, training processes, and the power of large language models like GPT.
Slides
Slide Presentation (7 slides)
Key Points
- GPT-4 is a powerful tool with extensive knowledge and capabilities, but it has vulnerabilities and limitations.
- Prompt engineering and fine-tuning are techniques to optimize GPT-4's performance, but they require technical expertise.
- Retrieval augmented generation and context window utilization can enhance GPT-4's model performance.
- GPT-4 is currently the most capable model, but RL (Reinforcement Learning) implementation is challenging.
- Transformers have limitations in terms of memory and computational work per token.
- The training process for models like GPT-4 involves predicting the next token and updating weights based on supervision.
- GPT-4 uses transformers to predict the next token in a sequence and has billions of parameters.
- The training data for GPT consists of various datasets, and the training process involves multiple stages, including pre-training, supervised fine-tuning, reward modeling, and reinforcement learning.
Summary
789 word summary
Welcome to Microsoft Build 2023, where brilliant minds gather to shape the future of technology. Embrace the limitless possibilities and let your creativity soar. GPT-4 is a powerful tool with extensive knowledge and capabilities, but it is vulnerable to attacks and has limitations. It is recommended to use GPT-4 in low-stakes applications with human oversight and as a source of inspiration. Prompt engineering and fine-tuning are techniques to optimize model performance, but they require technical expertise. Retrieval augmented generation and context window utilization can enhance model performance. Prompting techniques and adding relevant context are important for effective communication with the model. GPT-4 is currently the most capable model, but RL implementation is challenging. Fine-tuning models is becoming more accessible and efficient. It is important to provide clear instructions and enforce constraints on the model's output. Retrieval augmented models strike a balance between retrieval and memory-based approaches. Tools like calculators and code interpreters can assist in solving problems. It is crucial to find the right balance of expertise when asking questions. GPT-4 can imitate different levels of performance, but it requires explicit instructions for high-quality solutions. GPT-4 has limitations and should be used with caution in practical applications. The future may bring improvements and new projects to explore. The text excerpt discusses various aspects of GPT models and prompt engineering. It mentions the use of prompts in Auto Gp and the structure of prompts in the paper code react. The concept of policy and monte carlo tree research is highlighted, along with the use of python glue code. The idea of tree of thought and maintaining multiple completions for a prompt is discussed. The limitations of transformers and their inability to reflect or correct mistakes are mentioned. The importance of spreading out reasoning across tokens and the need for prompt engineering is emphasized. The difference between system one and system two thinking is explained. The limitations of transformers in terms of memory and computational work per token are discussed. The process of writing and refining sentences is compared to the generation process of transformers. The training process for models like Gp four is explained. Different assistant models and their rankings are mentioned. The use of base models for generating diverse outputs is discussed, along with the limitations of Rl chief models. The concept of symmetry and comparisons to leverage human judgment is mentioned. The loss of entropy in Rl models is highlighted. The importance of prompt comparison versus generation is explained, using the example of generating a haiku about paper clips. There is no definitive answer on why GPT models work better than base models, but one potential reason is the asymmetry in computational ease. RL models are preferred over base models because humans prefer RL tokens. The PPO models have been shown to work better in experiments. The RL pipeline involves training a reward model and using it to score the quality of completions. The S models are used to create completions and the reward model determines their quality. Reinforcement learning is then done with respect to the reward model. The completion quality is assessed through comparisons and a ranking system. The prompt is the same for all completions. The dataset for training consists of prompts and ideal responses written by human contractors. Base models can be tricked into acting as assistants by using specific prompt structures. Supervised fine-tuning involves collecting small but high-quality datasets. Prompt engineering is another approach where fake documents are used to prompt the model to perform tasks. The models are forced to multitask during pre-training, which helps them learn powerful representations. After training, the models are used for various downstream tasks. Training the models involves predicting the next token and updating the weights based on supervision. The models become more coherent and consistent over time. GPT, a neural network architecture with billions of parameters, uses transformers to predict the next token in a sequence. Each cell in the network analyzes the tokens before it. The input for GPT is a set of documents packed into rows with special end-of-text tokens marking the start of a new document. The tokens are organized into data batches for training. The training process involves specifying the model's parameters and hyperparameters, with larger models requiring more resources. The power of a model is determined by its training duration and the number of tokens it is trained on. Tokenization is used to convert text into integer sequences. The training data for GPT consists of various datasets, and the training process involves multiple stages, including pre-training, supervised fine-tuning, reward modeling, and reinforcement learning. The pre-training stage is computationally intensive. The talk provides insights into the emerging recipe for training GPT assistants and discusses the rapidly evolving ecosystem of large language models.