State of GPT | BRK216HFS

Introduction and Overview

In this section, Andrej Karpathy introduces himself and provides an overview of the talk on GPT (Generative Pre-trained Transformer) and large language models.

Introduction to GPT and Large Language Models

• Andrej Karpathy, an AI researcher and founding member of OpenAI, presents the state of GPT and the rapidly growing ecosystem of large language models.

• The talk is divided into two parts: training GPT Assistance and effectively using these assistants for applications.

Training Large Language Models

This section focuses on the process of training large language models like GPT Assistance.

Training Stages

• Four major stages in training large language models: pretraining, supervised finetuning, reward modeling, reinforcement learning.

• Each stage has a specific dataset, algorithm, and resulting model.

• Pretraining is the most computationally intensive stage, involving internet-scale datasets with thousands of GPUs over months of training.

• Finetuning stages require fewer resources with small numbers of GPUs over hours or days.

Pretraining Stage

• Pretraining is where most computational work happens (99% compute time).

• A large amount of data is gathered from various sources such as CommonCrawl, GitHub, Wikipedia, Books Archives.

• Data mixture is created by combining different datasets in specific proportions.

• Tokenization is performed to translate raw text into sequences of integers for input to the transformer model.

Pretraining Process

This section explains the steps involved in the pretraining process for creating a base model.

Data Gathering

• Datasets like CommonCrawl, GitHub, Wikipedia are mixed together based on given proportions to form the training set for GPT.

• The example shows a data mixture released by Meta called LLaMA.

Tokenization

• Preprocessing step to translate raw text into sequences of integers (tokens) that the GPT model can process.

• Lossless translation between text pieces and tokens using algorithms like byte pair encoding.

Hyperparameters

• Vocabulary size: typically a couple of 10,000 tokens.

• Context length: determines the maximum number of integers the GPT looks at when predicting the next integer in a sequence.

• Number of parameters: LLaMA has 65 billion parameters, trained on 1.4 trillion tokens.

Model Power and Training Costs

This section discusses the power of models based on their training duration and costs.

Model Power

• The power of a model should not be judged solely by its number of parameters.

• LLaMA, with fewer parameters than GPT-3, is more powerful due to longer training (1.4 trillion tokens).

Training Costs

• Rough hyperparameters for specifying transformer neural network and training settings are provided.

• For example, Meta used 2,000 GPUs, approximately 21 days of training, and several million dollars to train the 65B model.

Pretraining Process Continued

This section continues explaining the pretraining process for large language models.

Data Batches

• Tokens are organized into data batches for input to the transformer model.

• Arrays representing independent examples stacked up in rows with batch size (B) and maximum context length (T).

The summary covers the introduction to GPT and large language models, the stages involved in training these models, details about pretraining including data gathering and tokenization steps, information about model power and training costs.

Understanding the Transformer Neural Network Architecture

In this section, the speaker explains how the transformer neural network works and its role in predicting the next token in a sequence.

The Role of Each Cell in the Diagram

• Each cell in the diagram represents a step in the transformer neural network.

• The green cell takes into account all tokens before it (in yellow) and feeds that context into the transforming neural network.

• The transformer predicts the next token in a sequence based on this context.

Predicting Token Distributions

• Different parameters lead to slightly different predicted distributions for each cell.

• A probability distribution is specified for what comes next, based on vocabulary size.

• Supervision is applied to update transformer weights by comparing predicted and actual tokens.

Training and Coherence Improvement

• Training involves making correct predictions about what token comes next.

• Over time, as training progresses, the model produces more coherent and consistent samples from Shakespeare's works.

• Loss function plots show progress during model pretraining.

Fine-Tuning Transformers for Downstream Tasks

This section discusses how pretrained language models can be fine-tuned for specific tasks, such as sentiment classification.

General Representations Learned through Pretraining

• Pretrained language models learn powerful general representations during language modeling.

• These representations can be efficiently fine-tuned for various downstream tasks.

Shift from Sentiment Classification Approach

• Previously, sentiment classification involved training NLP models with positive and negative examples.

• The new approach is to ignore sentiment classification and instead focus on large language model pretraining.

Multitasking in Language Modeling

• Transformers are forced to understand the structure of text and various concepts while predicting the next token.

• GPT-1 was the initial model, followed by GPT-2, which introduced effective prompting techniques.

Prompt Engineering and Few-Shot Prompts

This section explores how prompt engineering can be used to trick language models into performing specific tasks.

Prompting for Task Performance

• Language models can be prompted effectively to perform tasks by arranging fake documents.

• Few-shot prompts involve using questions to guide the completion of a document.

Evolution of Base Models

• Various base models have been trained, but not all are publicly available.

• GPT-4 base model was never released, while GPT-3 and GPT-2 base models are accessible through different means.

Base Models vs. Assistants

• Base models aim to complete documents rather than provide direct answers or responses.

Introduction to GPT Assistant and Base Model

The base model of GPT (Generative Pre-trained Transformer) can be used as a document completer, but it is not very reliable in practice. It requires the user to input a query at the end, and the base model will attempt to provide an answer. However, this approach has limitations.

• The base model of GPT can be used as a document completer.

• It is not very reliable in practice.

• The user needs to input a query at the end for the base model to generate an answer.

• This approach has limitations and does not work well.

Supervised Fine-tuning for GPT Assistants

To create more reliable GPT assistants, supervised fine-tuning is used. In this stage, high-quality datasets are collected by human contractors who gather data in the form of prompts and ideal responses. These datasets are then used for language modeling instead of using internet documents as training data.

• Supervised fine-tuning is used to create more reliable GPT assistants.

• Human contractors collect high-quality datasets consisting of prompts and ideal responses.

• Language modeling is performed on these datasets instead of using internet documents as training data.

Example Demonstration of Prompt and Ideal Response

An example demonstration shows how human contractors provide prompts and ideal responses for training GPT assistants. Contractors follow extensive labeling instructions to ensure helpful, truthful, and harmless responses.

• Human contractors provide prompts and ideal responses for training GPT assistants.

• Contractors follow extensive labeling instructions.

• Prompt examples include writing short introductions or explanations on specific topics.

Reinforcement Learning from Human Feedback (RLHF)

RLHF is a pipeline that involves both reward modeling and reinforcement learning. In the reward modeling step, data collection shifts to comparisons, where multiple completions generated by the SFT model are ranked by human evaluators. The reward model is trained based on these rankings.

• RLHF involves reward modeling and reinforcement learning.

• Data collection shifts to comparisons of completions generated by the SFT model.

• Human evaluators rank the completions to train the reward model.

Training the Reward Model

The reward model is trained to score the quality of completions for prompts. A loss function is formulated using ground truth rankings provided by human evaluators, and the model is trained to make reward predictions consistent with these rankings.

• The reward model is trained to score completion quality for prompts.

• Ground truth rankings provided by human evaluators are used in training.

• A loss function is formulated to train the reward model.

Reinforcement Learning with Reward Model

Reinforcement learning is performed using a large collection of prompts and completions. The SFT model generates completions, which are then scored based on their quality using the fixed reward model. Language modeling loss function is applied while weighing it with rewards indicated by the reward model.

• Reinforcement learning uses a large collection of prompts and completions.

• Completions generated by the SFT model are scored using the fixed reward model.

• Language modeling loss function is applied while considering rewards from the reward model.

This summary covers key points from each section of the transcript in a clear and concise manner, making use of timestamps when available.

Reinforcement Learning from Human Feedback (RLHF) Pipeline

In this section, the speaker explains the RLHF pipeline and its purpose in training models.

RLHF Pipeline

• The RLHF pipeline involves sampling tokens and assigning them a slightly higher probability for the future.

• This process is repeated on multiple prompts and batches to create a policy that generates desired tokens.

• The resulting model is then deployed as an RLHF model, such as ChatGPT.

• Other models like Vicuna-13B are SFT models, while base models are also available.

• RLHF models have been found to work better based on experiments and human preferences.

Why RLHF Works Better

This section explores one potential reason why RLHF models work better than other models.

Asymmetry between Comparison and Generation

• Comparing generated outputs is computationally easier for humans compared to generating new content.

• Humans can judge the quality of different outputs more easily than creating high-quality content from scratch.

• This asymmetry allows leveraging human judgment through comparisons to improve model performance.

• However, RLHF models may lose some entropy and produce less varied results compared to base models.

Use Cases for Base Models

This section discusses scenarios where using base models might be preferred over RLHF models.

Base Models with High Entropy

• Base models have high entropy, resulting in diverse output with lots of variation.

• They are suitable for tasks like generating more items similar to a given set or prompt.

• For example, generating cool Pokemon names can benefit from the high entropy of base models.

Available Assistant Models

This section provides an overview of available assistant models and their rankings.

Ranking of Assistant Models

• A team at Berkeley ranked various assistant models using Elo ratings.

• GPT-4 is considered one of the best models, followed by Claude and GPT-3.5.

• Other models like Vicuna and Koala may also be available as weights.

• The top three models mentioned are RLHF models, while others are SFT models.

Applying GPT Assistant Model to a Problem

This section explores how to apply the GPT assistant model to specific problems using a concrete example.

Example: Comparing State Populations

• Suppose you want to compare the populations of California and Alaska for an article or blog post.

• The process involves gathering population data from sources like Wikipedia.

• Calculations may require tools like calculators for accuracy.

• Reflection and sanity checks help ensure the output makes sense in context.

Timestamps were used as requested, but some sections had fewer bullet points due to the nature of the content.

Writing and Training GPT

In this section, the speaker discusses the process of writing sentences and training a GPT (Generative Pre-trained Transformer) model. The differences between human sentence creation and how GPT processes tokens are explained.

Sentence Creation Process in GPT

• When creating sentences, there is a lot happening internally in terms of internal monologue.

• From the perspective of a GPT model, sentences are just sequences of tokens that it reads or generates.

• GPT models have about 80 layers of reasoning but still need to imitate the next token without reflecting or correcting mistakes like humans do.

• Transformers in GPT models are like token simulators and do not have separate inner monologue streams.

Cognitive Advantages of Transformers

• Transformers in GPT models have a large fact-based knowledge across various areas due to their billions of parameters.

• They have a relatively large working memory within their context window, allowing them to remember anything inside it.

• However, transformers lack the ability to reflect, sanity check, or correct mistakes along the way.

Prompts and Reasoning in Transformers

This section explores how prompts can compensate for cognitive differences between human brains and language models (LLMs). It also discusses spreading out reasoning across multiple tokens and techniques for improving transformer performance.

Reasoning Limitations in Transformers

• Transformers cannot perform extensive reasoning per token; complex questions require spreading out reasoning across more tokens.

• "These transformers need tokens to think" - expecting transformers to answer complex questions in a single token is unrealistic.

Techniques for Improving Transformer Performance

• Few-shot prompts can guide the transformer's evaluation by imitating templates shown in examples.

• Conditioning the transformer to show its work and making it perform slower reasoning over time increases the likelihood of success.

• Self-consistency techniques involve trying multiple times and selecting the best outcome, allowing transformers to recover from poor initial choices.

Transformers' Limitations and Prompting Strategies

This section highlights limitations faced by transformers during sequence generation and explores prompting strategies to overcome these limitations.

Transformers' Inability to Recover from Mistakes

• Transformers, unlike humans, cannot recover from poor token choices during sequence generation.

• They continue generating sequences even if they know it won't work out, as they are stuck with every sampled token.

Prompting Strategies for Overcoming Limitations

• By asking LLM models if they met the assignment or prompt requirements, we can identify when they have made mistakes.

• LLM models need explicit prompts to revisit and correct their mistakes; they do not check by themselves.

Planning and Prompt Engineering for LLMs

This section discusses the importance of planning and prompt engineering in the context of Language Model Models (LLMs). It explores recent research papers that propose techniques for maintaining multiple completions for prompts and scoring them along the way. The use of Python Glue code and tree search algorithms is also highlighted.

Tree of Thought Paper

• The paper "Tree of Thought" proposes maintaining multiple completions for prompts and scoring them along the way.

• Prompt engineering is being explored to bring back some of the abilities we have in our brain for LLMs.

• Python Glue code is used in conjunction with individual prompts, requiring a symbiosis between the two.

• There is a parallel between this approach and AlphaGo's policy and Monte Carlo Tree Search.

General Techniques for Prompt Engineering

• Researchers are exploring more general techniques beyond simple question-answer prompts.

• The paper "React" presents an example where answers to prompts are structured as sequences of thought-action-observation, allowing model tool use.

• Another project called AutoGPT allows recursive task breakdown but may not work well currently.

Understanding LLM Behavior: Imitation vs Success

This section delves into an interesting aspect of LLM behavior - their inclination towards imitation rather than success. It explains how transformers trained on language modeling cannot differentiate between low-quality and high-quality solutions by default.

Transformers' Training Data

• Transformers are trained on datasets with varying performance qualities, including both incorrect student solutions and expert answers.

• By default, transformers want to imitate all types of solutions due to their training on language modeling.

Asking for Good Performance

• To improve transformer performance, specific prompts can be used to condition it on getting the right answer.

• Asking for step-by-step reasoning or ensuring the right answer can help the transformer focus on high-quality solutions.

• It is important to find the right balance and not ask for too much intelligence, as it may lead to out-of-distribution or unrealistic responses.

Enhancing LLMs with Tools and Computation

This section explores the idea of enhancing LLMs by providing them with tools and computational abilities. It emphasizes the need to explicitly inform transformers about their strengths and weaknesses.

Giving LLMs Computational Tools

• LLMs can be equipped with calculators, code interpreters, search capabilities, etc., similar to how humans rely on tools when solving problems.

• Transformers may not know what they don't know by default, so it's important to explicitly inform them about their limitations and provide instructions on using specific tools.

Specifying Strengths and Weaknesses

• Prompting transformers about their proficiency in certain tasks can guide them towards using appropriate tools.

• For example, if a transformer is not good at mental arithmetic, it can be instructed to use a calculator for large number operations.

Conclusion

The transcript covers various aspects related to planning, prompt engineering, understanding LLM behavior, and enhancing LLMs with tools. It highlights the importance of prompt engineering techniques such as maintaining multiple completions for prompts and scoring them along the way. The inclination of transformers towards imitation rather than success is discussed, emphasizing the need for specific prompts that condition them on achieving high-quality solutions. Additionally, providing transformers with computational tools helps enhance their problem-solving capabilities.

New Section

In this section, the speaker discusses the concept of a transformer as a working memory and its relevance to the task. They also introduce the idea of retrieval-augmented generation and provide an example of LlamaIndex.

Transformer as Working Memory

• A transformer functions as a working memory for tasks.

• Loading relevant information into the working memory allows the model to access it immediately.

• Retrieval-augmented generation is an area of interest for many researchers.

• LlamaIndex is provided as an example of a data connector that indexes different types of data and makes it accessible to LLMs.

New Section

This section focuses on the process of indexing relevant documents, splitting them into chunks, embedding them, and storing them in a vector store. It also explains how these chunks can be fetched at test time based on queries.

Indexing and Embedding Data

• Relevant documents are split into chunks and embedded to obtain embedding vectors representing the data.

• These embedding vectors are stored in a vector store.

• At test time, queries are made to fetch chunks that may be relevant to the task.

• The fetched chunks are then used in generating outputs.

New Section

Here, the speaker emphasizes the importance of referencing primary documents while solving problems using transformers. They draw parallels between looking up information in textbooks or library documentation and transformers' need for external references.

Referencing Primary Documents

• Transformers have extensive memory but benefit from referencing primary documents.

• Looking up information in textbooks or library documentation is similar to what transformers should do.

• Transformers should have some memory over how documentation works but should primarily rely on looking it up when needed.

New Section

The speaker briefly introduces the concept of constraint prompting, which involves forcing a certain template in the outputs of LLMs. They mention an example called Guidance from Microsoft that enforces JSON output.

Constraint Prompting

• Constraint prompting techniques enforce specific templates in the outputs of LLMs.

• Guidance is an example where the output is constrained to be in JSON format.

• Probabilities of different tokens are manipulated to ensure compliance with the desired template.

New Section

This section discusses the benefits and challenges of fine-tuning models. The speaker mentions parameter-efficient fine-tuning techniques like Laura and highlights the availability of open-source base models.

Fine-Tuning Models

• Fine-tuning involves changing the weights of a model to optimize its performance.

• Techniques like parameter-efficient fine-tuning make it more accessible and efficient.

• Open-source high-quality base models are available for fine-tuning.

• Fine-tuning requires technical expertise, human data contractors, and complex data pipelines.

New Section

The speaker provides recommendations for approaching tasks using transformers. They suggest breaking down tasks into achieving top performance and optimizing performance, emphasizing prompt engineering and experimentation with few-shot examples.

Recommendations for Task Approach

• Break down tasks into achieving top performance and optimizing performance.

• GPT-4 model currently offers the best performance.

• Use detailed prompts with task content, relevant information, and instructions.

• Consider psychology-like aspects when designing prompts for LLMs.

• Experiment with prompt engineering techniques found online.

• Explore few-shot examples to enhance task understanding.

New Section

In this section, the speaker discusses considerations when working on prompt engineering, including thinking through the psychology of LLMs. They also caution against attempting to implement reinforcement learning from human feedback (RLHF) without sufficient expertise.

Prompt Engineering and RLHF

• Prompt engineering requires thinking through the psychology of LLMs.

• Implementing RLHF is challenging, unstable, and not beginner-friendly.

• Fine-tuning models are more technically involved but achievable.

• RLHF implementation is not advised due to complexity and rapid changes in the field.

New Section

The speaker concludes by summarizing their recommendations for approaching tasks using transformers. They emphasize achieving top performance with GPT-4 model and optimizing performance through prompt engineering.

Summary of Recommendations

• Achieve top performance using GPT-4 model.

• Use detailed prompts with relevant information and instructions.

• Consider prompt engineering techniques to enhance task understanding.

• Optimize performance by experimenting with few-shot examples.

New Section

In this section, the speaker discusses prompt engineering, fine-tuning models, and optimizing costs when using language models.

Prompt Engineering and Fine-Tuning Models

• The speaker suggests focusing on prompt engineering to improve the performance of language models.

• It is recommended to stick with prompt engineering for a while before considering fine-tuning a model for specific applications.

• Fine-tuning can be more time-consuming and complex compared to prompt engineering.

• RLHF (Reinforcement Learning from Human Feedback) is mentioned as a potentially better option than SFT (Supervised Fine-Tuning), but it requires more involvement and effort.

• To optimize costs, exploring lower capacity models or using shorter prompts is suggested.

Limitations of Language Models

• The speaker highlights that there are several limitations to language models that should be kept in mind for all applications.

• These limitations include biases, fabrication or hallucination of information, reasoning errors, struggles in certain application classes, knowledge cut-offs (limited information beyond a certain date), and susceptibility to various attacks such as prompt injection, jailbreak attacks, and data poisoning attacks.

Recommendations for Using Language Models

• LLMs (Language Models) are recommended to be used in low-stakes applications.

• Human oversight should always be combined with LLMs to ensure accuracy and reliability.

• LLMs should be used as sources of inspiration and suggestions rather than relying solely on them as autonomous agents performing tasks.

New Section

In this section, the speaker expresses appreciation for GPT-4 and emphasizes its capabilities. They also provide an example of how to use GPT-4 through code.

Appreciation for GPT-4

• The speaker describes GPT-4 as an amazing artifact with extensive knowledge across various domains.

• GPT-4 is capable of performing tasks such as math and coding.

• There is a thriving ecosystem of additional tools and features being built around GPT-4.

Example of Using GPT-4

• The speaker demonstrates how to use GPT-4 through code to ask it a question or prompt it for a response.

• An example prompt is given: "Can you say something to inspire the audience of Microsoft Build 2023?"

• The speaker shares the verbatim response generated by GPT-4, which includes inspiring words welcoming attendees and acknowledging their role in shaping the future.

Please note that these summaries are based on the provided transcript and may not capture all the details or nuances from the video.