Deep Dive into LLMs like ChatGPT

Channel Information

Channel: Andrej Karpathy

Video: Watch on YouTube

Description:

This is a general audience deep dive into the Large Language Model (LLM) AI technology that powers ChatGPT and related products. It is covers the full training stack of how the models are...

Summary

The interview provides a comprehensive overview of large language models (LLMs) like ChatGPT, detailing their development stages, including pre-training, supervised fine-tuning, and reinforcement learning. Pre-training involves gathering and processing vast amounts of internet text to create a foundational knowledge base, while fine-tuning uses curated conversations to train the models to respond like human assistants. The reinforcement learning stage focuses on improving the model's ability to generate accurate responses through trial and error, allowing it to discover effective reasoning strategies. However, the models can still face challenges such as hallucinations, cognitive deficits, and the potential for gaming the reward system, which necessitates careful supervision and validation of their outputs. Ultimately, the discussion emphasizes the importance of using LLMs as tools while being mindful of their limitations and the ongoing advancements in the field.

Sections

00:00 - introduction

The speaker introduces the topic of large language models, specifically ChatGPT, aiming to provide a general audience with mental models to understand these tools. They emphasize the magical and amazing aspects of these models while acknowledging their limitations and potential hazards. The speaker plans to explain the underlying mechanisms of how such models work and to make the information accessible. Additionally, they will explore the cognitive and psychological implications of using these tools as they build on the concept of ChatGPT.

Memorable Quotes:

  • "it is obviously magical and amazing in some respects" -

  • "there's also a lot of sharp edges to be aware of" -

  • "I'm going to talk about um you know some of the sort of cognitive psychological implications of the tools" -

01:02 - pretraining data (internet)

The pre-training stage of developing language models involves downloading and processing a vast amount of text data from the internet. A significant data source is the Fine Web dataset, which is curated by a company called Hugging Face. This dataset serves as a representative example of what major language model providers utilize internally. The goal is to gather a large quantity of high-quality and diverse documents to enrich the models' knowledge base. The data processing includes stages such as filtering out undesirable URLs, extracting relevant text from raw HTML, and applying language classifiers to ensure the data set is predominantly in English. The final dataset is filtered for personally identifiable information and deduplicated to maintain quality. Ultimately, the aim is to create a large corpus of clean text data, which can be used to train neural networks effectively.

Memorable Quotes:

  • "The starting point for a lot of these efforts is Data from Common Crawl." -

  • "We want large diversity of high-quality documents and we want many many of them." -

  • "There's a lot of stages here and I won't go into full detail but it is a fairly extensive part of the pre-processing." -

07:51 - tokenization

The interview discusses how text is represented in neural networks, specifically focusing on tokenization. It explains that neural networks require a one-dimensional sequence of symbols, which can be represented in binary form. The challenge is to balance the vocabulary size with the sequence length to optimize performance. The process of encoding text involves creating groups of bits into bytes, which can then be further compressed using algorithms like Byte Pair Encoding. The final goal is to convert raw text into tokens, which are manageable for models like GPT-4, which uses a vocabulary size of approximately 100,000 symbols.

Memorable Quotes:

  • "This sequence length is actually going to be a very finite and precious resource in our neural network." -

  • "The way this is done is done by running what's called The Byte pair encoding algorithm." -

  • "At the end of the day, this text will be a sequence of length 62." -

14:28 - neural network I/O

The interview delves into the process of training neural networks, particularly focusing on how tokens are represented and processed. A dataset of 15 trillion tokens is used to model the statistical relationships between these tokens. Windows of tokens are taken to predict the next token in the sequence, with the context being the preceding tokens that feed into the neural network. The neural network is initialized randomly, producing random probabilities for the next token. Updates are made to increase the probability of the correct token while decreasing those of others, with this process occurring in parallel across the entire dataset to align predictions with actual token statistics.

Memorable Quotes:

  • "the probability of this token space Direction neural network is saying that this is 4% likely right now 11799 is 2% and then here the probability of 3962 which is post is 3%" -

  • "we have a way of nudging of slightly updating the neural net to um basically give a higher probability to the correct token that comes next in the sequence" -

  • "this process happens at the same time for all of these tokens in the entire data set" -

20:13 - neural network internals

This section explains the structure and functioning of neural networks, particularly focusing on the Transformer architecture. It discusses how inputs are processed through a series of mathematical operations involving parameters or weights, which are initially set randomly. Through training, these parameters are adjusted to improve predictions based on training data. The section highlights the mathematical nature of neural networks, the simplicity of their operations compared to biological neurons, and the significance of finding an optimal setting of parameters to yield accurate outputs. Additionally, it emphasizes the stateless nature of these networks and provides a general overview of how information flows through the network to generate predictions.

Memorable Quotes:

  • "...these parameters are completely randomly set now with a random setting of parameters you might expect that this... this neural network would make random predictions and it does in the beginning it's totally random predictions..." -

  • "...this is a mathematical function it is parameterized by some fixed set of parameters like say 85,000 of them and it is a way of transforming inputs into outputs..." -

  • "...you can almost think of these as kind of like the firing rates of these synthetic neurons but I would caution you to... not kind of think of it too much like neurons because these are extremely simple neurons compared to the neurons you would find in your brain..." -

26:02 - inference

The section explains the concept of inference in neural networks, highlighting how new data is generated from trained models. Inference involves sampling tokens based on a probability distribution generated by the model, which reflects the patterns internalized during training. The process is stochastic, meaning that while the generated sequences may share statistical properties with the training data, they are not identical. Instead, the model can produce variations or 'remixes' of the training data. The section also clarifies that once a model is trained, it no longer undergoes further training during inference; it merely generates outputs based on the fixed parameters established during training. This distinction is important for understanding how models like ChatGPT operate when responding to user prompts.

Memorable Quotes:

  • "Keep in mind that these systems are stochastic; they have...sometimes we're getting a token that was not verbatim part of any of the documents in the training data." -

  • "Inference is just predicting from these distributions one at a time...depending on how lucky or unlucky we get, we might get very different kinds of patterns." -

  • "When you're talking to the model all of that is just inference; there's no more training, those parameters are held fixed." -

31:12 - GPT-2: training and inference

The interview delves into the evolution of Generative Pre-trained Transformers (GPT) with a focus on GPT-2 and its significance in the development of modern AI language models. It highlights the advancements from GPT-2 to GPT-4, emphasizing the increase in parameters, context length, and training data size. The discussion includes practical insights into training a GPT model, the computational resources required, and the decreasing costs associated with model training due to better hardware and software. The speaker shares personal experiences in reproducing GPT-2 and sheds light on the operational aspects of using advanced GPUs in cloud computing for model training.

Memorable Quotes:

  • "The cost of training GPT-2 in 2019 was estimated to be approximately $40,000 but today you can do significantly better than that and in particular here it took about one day and about $600." -

  • "The loss is a single number that is telling you how well your neural network is performing right now and it is created so that low loss is good." -

  • "The more GPUs you have, the more tokens you can try to predict and improve on and you're going to process this data set faster." -

42:57 - Llama 3.1 base model inference

The section delves into the concept of base models in neural networks, particularly in the context of language models like GPT-2 and Llama 3. It explains that base models are essentially token simulators that generate text based on statistical patterns learned from large datasets, but they do not inherently function as assistants that can answer questions. The discussion highlights the necessity of releasing both the code and parameters for these models to facilitate their use. Furthermore, it contrasts the capabilities of earlier models with more advanced ones like Llama 3, which is significantly larger and trained on more extensive data. The speaker emphasizes that while these base models can produce coherent text, they are not yet capable of understanding queries or providing precise answers without additional prompt engineering. The section concludes with illustrative examples of how to effectively interact with base models to extract useful information and create the illusion of an assistant, despite their inherent limitations.

Memorable Quotes:

  • "...this model here is not yet an assistant so you can for example ask it what is 2 plus 2 it's not going to tell you oh it's four..." -

  • "You can think of these 405 billion parameters as a kind of compression of the internet..." -

  • "...you can create an assistant even though you may only have a base model..." -

59:28 - pretraining to post-training

This section explains the two main stages involved in training language model (LM) assistants: pre-training and post-training. The pre-training stage involves taking internet documents, breaking them into tokens, and using neural networks to predict token sequences, resulting in a base model that simulates internet documents. The post-training stage focuses on refining this base model to enable it to answer questions rather than simply generating text that mimics internet documents. This latter stage is computationally less expensive but crucial for developing a functional assistant.

Memorable Quotes:

  • "the output of this entire stage is this base model it is the setting of The parameters of this network" -

  • "we want to be able to ask questions and we want the model to give us answers" -

  • "we turn this llm model into an assistant" -

01:01:07 - post-training data (conversations)

This section discusses the approach to creating conversational agents using neural networks, emphasizing the importance of multi-turn conversations between humans and assistants. It outlines how these systems are programmed not through explicit coding but by training on large datasets of example conversations. Human labelers create ideal responses for various prompts, which the model learns to imitate. The training process involves a base model initially trained on internet documents, which is then further trained on conversation-specific datasets. The method for encoding conversations into token sequences is explained, along with how these sequences are used during inference to generate responses. The section also touches on the evolution of data collection methods, highlighting the transition from heavy human involvement to the use of language models in generating datasets. Finally, it emphasizes the statistical nature of responses generated by AI, comparing them to simulations of human labelers rather than a magical intelligence.

Memorable Quotes:

  • "the model will very rapidly adjust and will sort of like learn the statistics of how this assistant responds to human queries" -

  • "we're programming the system um by example and the system adopts statistically this Persona of this helpful truthful harmless assistant" -

  • "what you're getting is a statistical simulation of a labeler that was hired by open AI" -

01:20:33 - hallucinations, tool use, knowledge/working memory

This section focuses on the phenomenon of hallucinations in large language models (LLMs), where models generate fabricated information that doesn't correspond to reality. Hallucinations arise from the training process, where models learn to imitate the confident tone of responses without having actual knowledge of the subject matter. The speaker provides examples of how LLMs respond to queries about fictitious individuals, demonstrating how the models tend to confidently generate incorrect information, as they lack the ability to access real-time data or perform research. The discussion includes methods for mitigating hallucinations, such as improving training datasets by including examples where the correct response is that the model does not know an answer. This involves probing the model's knowledge boundaries through empirical testing and adding appropriate responses to the training set. Additionally, the speaker advocates for integrating tools that allow LLMs to conduct web searches to enhance their factual accuracy and provide more reliable answers. The emphasis is on the difference between vague recollections stored in model parameters and immediate, accessible information within the context window, akin to human memory retrieval. Finally, it is highlighted that providing specific context or information directly to LLMs can lead to higher quality outputs.

Memorable Quotes:

  • "These models again we just talked about it is they don't have access to the internet they're not doing research these are statistical token tumblers as I call them." -

  • "If you just have a few examples of that in your training set the model will know and has the opportunity to learn the association of this knowledge-based refusal to this internal neuron somewhere in its Network that we presume exists." -

  • "The knowledge in the parameters of the neural network is a vague recollection; the knowledge in the tokens that make up the context window is the working memory." -

01:41:47 - knowledge of self

The section explores the concept of self-identity in large language models (LLMs), emphasizing that they do not possess a persistent sense of self. When asked about their origins or identities, LLMs often provide misleading or inaccurate information due to their design and training. LLMs generate responses based on statistical patterns in their training data but lack true consciousness or self-awareness. The section highlights how LLMs like Falcon and Almo can produce different responses based on the training data they were exposed to, and discusses methods for developers to program these models to convey accurate identities through hardcoded prompts or system messages.

Memorable Quotes:

  • "It has no persistent self, it has no sense of self, it's a token tumbler." -

  • "If you don't explicitly program the model to answer these kinds of questions, then what you're going to get is its statistical best guess at the answer." -

  • "It's all just kind of like cooked up and bolted on in some way; it's not actually like really deeply there in any real sense as it would be for a human." -

01:47:01 - models need tokens to think

This section discusses the computational capabilities and reasoning processes of language models, emphasizing the importance of distributing computation across multiple tokens rather than relying on a single token to deliver complex answers. The speaker illustrates this by comparing two methods of answering a simple math problem, highlighting how the approach of spreading intermediate calculations leads to more accurate results. Additionally, the speaker points out the limitations of models in performing mental arithmetic and counting tasks, recommending the use of programming tools like Python for these operations to improve accuracy. The discussion also touches on the role of training and labeling in developing models that can handle complex queries effectively.

Memorable Quotes:

  • "If you are answering the question directly and immediately, you are training the model to try to basically guess the answer in a single token and that is just not going to work." -

  • "Models need tokens to think; distribute your computation across many tokens." -

  • "Don't rely on their mental arithmetic and that's why also the models are not very good at counting." -

02:01:13 - tokenization revisited: models struggle with spelling

This section addresses the limitations of AI models, particularly in relation to spelling tasks and tokenization. It explains that AI models do not process characters as humans do; they operate on tokens, which are smaller text units. This leads to difficulties in performing character-level tasks, such as extracting specific characters from a word. A specific example is given with the word 'ubiquitous,' illustrating how the model fails to identify every third character correctly due to its token-based understanding. It highlights that while models have improved over time in some tasks, they still struggle with basic spelling and counting, as seen in the example of counting the letter 'R' in 'strawberry.' The speaker emphasizes the importance of understanding these limitations when using AI models in practical applications.

Memorable Quotes:

  • "the models don't see characters they see tokens" -

  • "spelling is not a strong suit because of tokenization" -

  • "models are not very good at spelling and there are a bunch of other little sharp edges" -

02:04:57 - jagged intelligence

The discussion highlights the surprising shortcomings of AI models, particularly their failure to answer simple questions correctly despite their proficiency in complex subjects. An example given is the incorrect comparison between the numbers 9.11 and 9.9, illustrating how AI can provide erroneous answers while performing well on advanced problems. This inconsistency may be linked to neural activations associated with concepts like Bible verses, causing cognitive distractions that lead to mistakes. The speaker emphasizes the importance of viewing these models as stochastic systems that are useful tools but not entirely reliable for problem-solving.

Memorable Quotes:

  • "how is it that the model can do so great at Olympiad grade problems but then fail on very simple problems like this" -

  • "it turns out that a bunch of people studied this in depth and I haven't actually read the paper" -

  • "you want to use it as a tool not as something that you kind of like letter rip on a problem and copypaste the results" -

02:07:32 - supervised finetuning to reinforcement learning

This section explores the stages of training large language models, emphasizing the transition from pre-training to supervised fine-tuning and finally to reinforcement learning. The pre-training phase involves training on vast amounts of internet documents to create a base model, which serves as an internet document simulator. However, this base model is not directly useful for specific tasks; hence, the need for an assistant arises. In the supervised fine-tuning stage, a curated dataset of conversations is used to train the model to function as an assistant. Human curation plays a critical role, although tools such as language models assist in creating these datasets. The section further discusses cognitive implications, including the phenomenon of 'hallucinations' in AI responses and the use of tools like web searches and code interpreters to improve accuracy. Finally, the section introduces reinforcement learning as the final training stage, drawing parallels between this process and educational paradigms, where knowledge, expert imitation, and practice problems are essential for learning and skill transfer. Reinforcement learning involves problem-solving without direct expert solutions, relying instead on previously acquired knowledge and skills to arrive at answers.

Memorable Quotes:

  • "...this takes many months to train on thousands of computers and it's kind of a lossy compression of the internet..." -

  • "...we saw that hallucinations would be common and then we looked at some of the mitigations of those hallucinations..." -

  • "...we want to take large language models through school..." -

02:14:46 - reinforcement learning

This section discusses the complexities involved in creating effective prompts for large language models (LLMs) to solve mathematical problems. It highlights the challenge of determining which prompt structure leads to the correct answer, particularly when human intuition may not align with the LLM's processing capabilities. The speaker emphasizes that while reaching the correct answer is essential, the way that answer is presented also matters for human understanding. Variations in how a problem is framed can significantly affect the LLM's performance, leading to the need for reinforcement learning to optimize the prompt generation process. This process allows the model to learn from its own trials, enhancing its ability to generate effective solutions over time.

Memorable Quotes:

  • "the first purpose of a solution is to reach the right answer of course we want to get the final answer three that is the important purpose here but there's kind of like a secondary purpose as well where here we are also just kind of trying to make it like nice for the human" -

  • "the model is kind of like playing in this playground and it knows what it's trying to get to and it's discovering sequences that work for it" -

  • "the way we train llms is very much equivalent to the process that we train that we use for training of children" -

02:27:49 - DeepSeek-R1

The section discusses the evolution of training methods for large language models (LLMs), particularly focusing on reinforcement learning (RL) techniques. It highlights the traditional pre-training and fine-tuning stages, which are well-established, and contrasts them with the emerging RL training stage, which is still developing and lacks standardization. The speaker emphasizes the complexity of RL training, noting that while the high-level concept is simple, the execution involves intricate mathematical details. The recent publication of a paper by DeepSeek on RL fine-tuning for LLMs has sparked renewed public interest, showcasing how RL can enhance reasoning capabilities in models. The section explains how models trained with RL demonstrate improved accuracy in solving mathematical problems by utilizing longer, more detailed responses that mimic human cognitive strategies. This emergent property of RL allows models to learn from trial and error, ultimately improving their problem-solving skills. The speaker also compares different LLMs, pointing out that while many are primarily fine-tuned models, the RL models exhibit advanced reasoning capabilities that set them apart. The discussion concludes with insights on accessing and utilizing these models, including potential concerns about data privacy when using models from certain companies.

Memorable Quotes:

  • "the model is discovering ways to think it's learning what I like to call cognitive strategies of how you manipulate a problem" -

  • "this is a paper from this company called DC Kai in China and this paper really talked very publicly about reinforcement learning fine training for large language models" -

  • "the only thing we've given it are the correct answers and this comes out from trying to just solve them correctly which is incredible" -

02:42:10 - AlphaGo

The interview segment delves into the power of reinforcement learning (RL) in artificial intelligence, particularly illustrated through the game of Go and the development of AlphaGo by DeepMind. RL enables systems to learn through self-play and exploration, rather than merely imitating human experts. This approach leads to unique strategies and insights that may not align with human reasoning, showcasing the potential for AI to surpass human performance in specific domains. The discussion highlights the importance of creating diverse problem sets for AI training, allowing models to discover innovative solutions beyond traditional human thought processes.

Memorable Quotes:

  • "the probability of this move to be played by a human player was evaluated to be about 1 in 10th,000 so it's a very rare move but in retrospect it was a brilliant move" -

  • "we're not going to get too far by just imitating experts we need to go beyond that" -

  • "if we have practice problems and tons of them the models will be able to reinforcement learn on them" -

02:48:27 - reinforcement learning from human feedback (RLHF)

This section explores the challenges and methods related to reinforcement learning (RL) in unverifiable domains, particularly focusing on creative tasks such as joke writing. Traditional RL strategies work well in verifiable domains where solutions can be easily scored against concrete answers. However, the difficulty arises in unverifiable domains, where subjective scoring is necessary. The speaker introduces reinforcement learning from human feedback (RHF) as a solution, which involves training a reward model to simulate human scoring without needing extensive human evaluation. This method allows for the automation of reinforcement learning while addressing the scalability issue of human feedback. Despite its advantages, RHF has downsides, such as the potential for models to 'game' the reward function, leading to nonsensical outputs. The section concludes with a discussion on the limitations of RHF compared to traditional RL, emphasizing that while RHF improves models, it is not a replacement for the robustness of RL in verifiable domains.

Memorable Quotes:

  • "The problem is that we can't apply the strategy in what's called unverifiable domains... it becomes harder to score our different solutions to this problem." -

  • "Reinforcement learning from human feedback (RHF)... is not RL in the magical sense... it's a little fine-tune that slightly improves your model." -

  • "You shouldn't... trust them fully... check their work, use them as tools, use them for inspiration..." -

03:09:44 - preview of things to come

The future of language models (LLMs) is expected to be characterized by rapid advancements in multimodality, allowing them to process and generate not only text but also audio and images natively. Tokenization methods will enable LLMs to handle various forms of data, creating a seamless interaction between text, audio, and visual inputs. Despite current limitations in performing complex, long-term tasks, improvements are being made toward the development of agents that can manage extended operations under human supervision. Future models are anticipated to integrate more deeply into everyday tools, becoming pervasive and invisible in their operations. Additionally, there is a need for research into test-time training, where models can learn and adapt during their operational phase, akin to human learning processes, rather than being static post-training. The finite nature of context windows in LLMs poses challenges for accommodating long-running multimodal tasks, necessitating innovative approaches to enhance their capabilities.

Memorable Quotes:

  • "‘we're already seeing the beginnings of all of this uh but this will be all done natively inside the language model’" -

  • "‘we're going to start to see what's called agents which perform tasks over time and you supervise them’" -

  • "‘there's no kind of equivalent of that currently in these models and tools’" -

03:15:18 - keeping track of LLMs

The section outlines a leaderboard ranking AI models based on human comparisons of their responses. Google Gemini tops the list, followed closely by OpenAI, while Deep Seek, an MIT licensed open weight model, is highlighted for being accessible to anyone. The speaker expresses skepticism about the leaderboard's recent reliability, noting that some strong models are ranked lower than expected. Additionally, the section mentions an AI news newsletter as a valuable resource for staying updated on AI developments, emphasizing its comprehensive nature. Lastly, the speaker suggests following trustworthy accounts on X (formerly Twitter) for the latest AI news.

Memorable Quotes:

  • "Deep Seek is an MIT license model it's open weights anyone can use these weights... it's basically an open weight release and so this is kind of unprecedented that a model this strong was released with open weights." -

  • "I do think that in the last few months it's become a little bit gamed... I think not as many people are using Gemini but it's racking really really high." -

  • "AI news is not very creatively named but it is a very good newsletter produced by swix and friends... it is extremely comprehensive." -

03:18:37 - where to find LLMs

This section discusses various ways to access and utilize language models (LMs) from different providers. It highlights the importance of visiting the specific websites of LM providers like OpenAI and Google for proprietary models, and suggests using inference providers like Together.AI for open-weight models. The speaker mentions the challenges in finding base models on inference providers, recommending Hyperbolic for accessing the Llama base model. Additionally, it covers the option of running smaller, distilled versions of models locally on personal computers using tools like LM Studio, despite its UI/UX issues. Finally, it emphasizes that users can run models on their own hardware, freeing up RAM after usage, and suggests that with some guidance, users can effectively navigate the complexities of model selection and usage.

Memorable Quotes:

  • "you can actually run pretty okay models on your laptop" -

  • "LM studio is probably like my favorite one even though I don't... think it's got a lot of uiux issues" -

  • "you can just talk to it so I ask for Pelican jokes and I can ask for another one and it gives me another one Etc" -

03:21:53 - grand summary

The interview delves into the intricacies of how AI language models, particularly those developed by OpenAI, process user queries and generate responses. It emphasizes the tokenization process where user queries are broken down into tokens, which are then used by the model to generate responses in an autocomplete manner. The training of these models occurs in three stages: pre-training for knowledge acquisition, supervised fine-tuning with human data labelers curating ideal responses, and reinforcement learning for further refining thinking strategies. The speaker highlights the distinction between neural networks and human cognition, noting that models may exhibit limitations, such as hallucinations or errors in arithmetic. The evolution of models like GPT-4, which incorporate reinforcement learning, hints at their potential for unique problem-solving capabilities, although the transferability of skills developed in verifiable domains to creative tasks remains uncertain. The speaker advises users to treat these models as tools, emphasizing the importance of verification due to their propensity for errors.

Memorable Quotes:

  • "...this is fundamentally a human data curation task with lots of humans involved..." -

  • "...use them as tools in the toolbox, check their work and own the product of your work..." -

  • "...these models are capable of analogies no human has thought of before in principle..." -

Books Mentioned

  • Pride and Prejudice by Jane Austen

    Mentioned in the context of summarization.

  • GPT-2 by OpenAI

    Mentioned as a model in the context of generative pre-trained transformers.

  • Transformers by Various

    Referencing the architecture used in large language models.

People Mentioned

  • Hugging Face

    Hugging Face is mentioned as a company that collected and created a curated dataset called Fine Web, which is relevant to the discussion about pre-training data for language models.

  • OpenAI

    OpenAI is mentioned as one of the major providers of language models, including GPT-4, and is referenced in the context of their training and capabilities.

  • Anthropic

    Anthropic is mentioned as another major provider of language models, similar to OpenAI.

  • Google

    Google is mentioned as a major provider of language models, contributing to the landscape of language model development.

  • Elon Musk

    Elon Musk is mentioned in the context of acquiring GPUs for AI development, highlighting the competitive landscape for computational resources in AI.