Summary State of GPT | BRK216HFS - YouTube (Youtube) www.youtube.com
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One Line
GPT-4 is a powerful and versatile AI model that goes through a multi-step training process, with prompt engineering as a way to enhance performance, but it requires human oversight due to limitations and potential vulnerabilities.
Slides
Slide Presentation (9 slides)
Key Points
- GPT (Generative Pre-trained Transformer) is a large language model that goes through four stages of training: pre-training, supervised fine-tuning, reward modeling, and reinforcement learning.
- Prompting the models can be more effective than fine-tuning, and base models can be prompted to perform specific tasks.
- Supervised fine-tuning and reinforcement learning from human feedback are necessary to create actual GPT assistants.
- RL models are preferred over S models and base models in the training process, as they work better and leverage human judgment.
- Prompt engineering techniques can be used to improve the performance of GPT models, such as spreading out reasoning, using templates, and allowing them to revise their sequences.
- GPT models imitate tokens and lack cognitive abilities like internal dialogue or reflection, but they have large fact-based knowledge and working memory.
- Python glue code and careful prompt engineering are required to achieve desired results with prompt-based language models like GPT-4.
- It is important to consider the limitations of language models, such as biases, fabricating information, reasoning errors, knowledge cutoffs, and vulnerability to attacks.
Summaries
58 word summary
GPT's training process involves pre-training, supervised fine-tuning, reward modeling, and reinforcement learning. Different models are used, with GPT-4 being one of the best assistants. Prompt engineering can improve performance, but limitations such as biases and vulnerability to attacks should be considered. Human oversight is necessary in low-stakes applications. GPT-4 is a powerful tool accessible through simple code prompts.
115 word summary
Andre Carp, an AI researcher and founding member of OpenAI, discussed the state of GPT and its training process. The process involves pre-training, supervised fine-tuning, reward modeling, and reinforcement learning. Carp emphasized that base models can be prompted to perform tasks but require supervised fine-tuning and reinforcement learning for actual GPT assistants. Different types of models are used, with RL models leveraging human judgment. GPT-4 is one of the best assistant models available. Prompt engineering techniques can improve performance, but it's important to consider limitations such as biases and vulnerability to attacks. Language models should be used with human oversight in low-stakes applications. In conclusion, GPT-4 is a powerful tool accessible through simple code prompts.
313 word summary
Andre Carp, an AI researcher and founding member of OpenAI, discussed the state of GPT (Generative Pre-trained Transformer) and the training process for using it effectively. The training process involves pre-training, supervised fine-tuning, reward modeling, and reinforcement learning. Carp emphasized that base models are not systems but can be prompted to perform specific tasks. Supervised fine-tuning and reinforcement learning from human feedback are necessary to create actual GPT assistants. Reward modeling and reinforcement learning improve the model's performance.
Speaker B explained that they are currently training on yellow tokens and weighing the language modeling objective by the rewards indicated by the reward model. Tokens that receive a high score from the reward model are reinforced, while tokens that receive a low score are given slightly higher probabilities. Different types of models, such as base models, S models, and RL chip models, are used in the training process. RL models work better because they leverage human judgment. Base models are still preferred in scenarios where diverse outputs are desired.
There are various assistant models available, with GPT-4 being one of the best. Prompting is important for training and using the GPT assistant model. Techniques such as spreading out reasoning across more tokens, using templates, thinking step by step, and self-consistency can improve performance. Prompt engineering is being explored to recreate human-like abilities in GPT models.
GPT models imitate tokens and do not have the same cognitive abilities as humans. Prompt-based language models require careful prompt engineering and the use of python glue code. Techniques like retrieval-augmented generation and consumable string prompting can enhance model performance. However, it is important to consider the limitations of language models, such as biases and vulnerability to attacks. It is recommended to use language models in low-stakes applications with human oversight.
In conclusion, GPT-4 is a powerful tool with a vast amount of knowledge accessible through simple code prompts.
809 word summary
Andre Carp, an AI researcher and founding member of OpenAI, discussed the state of GPT (Generative Pre-trained Transformer) and the growing ecosystem of large language models. He divided his talk into two parts: training GPT and using it effectively for applications. The training process consists of four major stages: pre-training, supervised fine-tuning, reward modeling, and reinforcement learning. Pre-training involves gathering a large amount of data from various sources and tokenizing it into sequences of integers. The resulting base model is then fine-tuned using high-quality datasets for specific tasks. Carp mentioned that prompting the models can be more effective than fine-tuning. He also highlighted the evolutionary tree of base models, with GPT-3 being available as the Dev vinci model. Carp emphasized that base models are not systems and can only complete documents, but they can be prompted to perform specific tasks. However, to create actual GPT assistants, supervised fine-tuning and reinforcement learning from human feedback are necessary. In supervised fine-tuning, human contractors provide prompt-response pairs, which are used to train the model. Reward modeling and reinforcement learning further improve the model's performance by collecting data in the form of comparisons and ranking completion options based on a reward model. The reward model is then used during reinforcement learning to score the quality of completions and train the model accordingly.
Speaker B explains that they are currently training on yellow tokens and weighing the language modeling objective by the rewards indicated by the reward model. Tokens that receive a high score from the reward model are reinforced and given higher probabilities for the future, while tokens that receive a low score are given slightly higher probabilities. This process is repeated on many prompts and batches to create a policy. Different types of models, such as base models, S models, and RL chip models, are used in the training process. RL models are preferred by humans compared to S models and base models because they work better. The reason for this improvement is not fully understood, but it may have to do with the asymmetry between generating and comparing. Comparisons are easier for humans and can leverage human judgment to create a better model. RL models may lose some entropy compared to base models, resulting in less variation in their outputs. Base models are still preferred in certain scenarios where diverse outputs are desired.
The speaker mentions that there are various assistant models available, with GPT-4 being one of the best followed by Claude G3.5. Prompting is an important aspect of training and using the GPT assistant model. The speaker provides an example of comparing populations of California and Alaska, highlighting the internal monologue and computational processes involved in generating a sentence. From the GPT's perspective, it is just a sequence of tokens and each token receives the same amount of computational work. GPT models do not have the same cognitive abilities as humans and do not have internal dialogue or reflection. They imitate the next token without knowing what they are good at or correcting mistakes. However, they have large fact-based knowledge and relatively large working memory.
To get better results from GPT models, prompting techniques can be used. Spreading out reasoning across more tokens, using templates to show work, thinking step by step, and self-consistency can improve performance. Sampling multiple times and selecting the best samples can also be effective. GPT models cannot recover from bad tokens like humans, so techniques that allow them to look back or revise their sequences can be useful. Prompt engineering is an important aspect of recreating human-like abilities in GPT models. Techniques such as maintaining multiple completions for a prompt and scoring them along the way are being explored.
In conclusion, GPT models are trained using RL chip models and prompt engineering techniques can be used to improve their performance. While they may not have the same cognitive abilities as humans, they can imitate tokens
Prompt-based language models, such as GPT-4, require careful prompt engineering and the use of python glue code to achieve desired results. Similar to AlphaGo, these models employ tree search algorithms to expand and evaluate multiple prompts. The use of structured prompts, like thought-action-observation sequences, and tools like calculators and code interpreters can enhance performance. Retrieval-augmented generation, which incorporates relevant information into the model's working memory, is gaining interest. Techniques like consumable string prompting and fine-tuning can further improve model performance, although fine-tuning requires technical expertise and may involve complex data pipelines. It is important to consider the limitations of language models, such as biases, fabricating information, reasoning errors, knowledge cutoffs, and vulnerability to attacks. Therefore, it is recommended to use language models in low-stakes applications with human oversight and treat them as sources of inspiration rather than fully autonomous agents. Despite these limitations, GPT-4 is a powerful tool with a vast amount of knowledge accessible through simple code prompts.
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