Deep Dive into LLMs like ChatGPT | Highlights and Annotations by Gistr.

LLMs like ChatGPT are trained in three stages: pre-training on massive text data, supervised fine-tuning with human-labeled conversations, and reinforcement learning for improved reasoning. They predict text probabilistically, but are prone to errors ("hallucinations"). The video explains this process, highlighting capabilities, limitations, and future directions. This segment explains how raw text is converted into a format suitable for neural network processing. It describes the challenges of representing text as a one-dimensional sequence of symbols for neural networks, introducing the concept of tokenization. The presenter explains different methods of tokenization, including byte pair encoding, and the trade-off between vocabulary size and sequence length. The discussion emphasizes the importance of efficient text representation for computational efficiency in training large language models. This segment details the initial stages of building a large language model (LLM) like ChatGPT, focusing on data acquisition from sources like Common Crawl and the extensive preprocessing steps involved. It explains the challenges of gathering high-quality, diverse text data from the internet, filtering out unwanted content (malware, spam, etc.), and the process of reducing the massive dataset to a manageable size (around 44 terabytes in this example) while retaining crucial information. The presenter highlights the complexities of data cleaning and the choices involved in language filtering and personally identifiable information (PII) removal. This segment explains the fundamental structure of neural networks, illustrating how input data ("tokens") interacts with network parameters ("weights") through a complex mathematical expression. It details the training process, where initially random parameters are iteratively adjusted to align network outputs with patterns in the training data, analogous to tuning knobs on a DJ set to achieve a desired sound. The segment also provides a simplified example of the mathematical expression involved and transitions to a discussion of a real-world example.This segment showcases a visualization of a production-grade transformer neural network, highlighting its structure and information flow. It explains how token sequences are processed through layers of transformations (attention blocks, multi-layer perceptrons), generating intermediate values that ultimately lead to predictions. The analogy to "synthetic neurons" is made, emphasizing the simplicity of these artificial neurons compared to biological ones. The segment concludes by emphasizing the network's function as a parameterized mathematical function transforming inputs into outputs.This segment focuses on the inference stage, where the trained neural network generates new data. It explains the process of generating text by sampling tokens based on probability distributions produced by the network. The stochastic nature of this process is highlighted, showing how the model can generate sequences similar to, but not identical to, those in the training data, creating "remixes" of learned patterns. The segment emphasizes that inference involves predicting from distributions one token at a time, continuously feeding back predictions to generate longer sequences. This segment uses OpenAI's GPT-2 model as a concrete example to illustrate the training and inference processes. It details GPT-2's architecture (transformer network), parameters (1.5 billion), context length (124 tokens), and training data size (100 billion tokens). The segment discusses the cost of training GPT-2 in 2019 ($40,000) and how significantly this cost has decreased today (to around $100), due to improvements in data quality, hardware speed, and software optimization.This segment provides a researcher's perspective on the training process, showing a real-time visualization of training a GPT-2 model. It explains how each line in the visualization represents an update to the model's parameters, improving its prediction accuracy. The "loss" metric is introduced as a key indicator of the model's performance, decreasing as training progresses. The segment also shows examples of the model's output at different stages of training, illustrating how it evolves from generating random text to producing more coherent sentences.This segment discusses the computational resources required for training large language models. It describes the use of cloud-based computing resources, specifically an 8x NVIDIA H100 GPU node, and explains the cost-effectiveness of renting such resources. The segment concludes with a visual representation of an NVIDIA H100 GPU, highlighting its suitability for the parallel computations involved in training neural networks. This segment delves into the core process of training the neural network. It explains how the model learns the statistical relationships between tokens by analyzing sequences of tokens (windows) and predicting the next token in the sequence. The presenter describes the process of updating the neural network's weights to improve its predictions, emphasizing the iterative nature of the training process and the parallel processing of large batches of tokens. The segment provides a high-level overview of the training methodology without getting bogged down in excessive technical details. This segment explains the massive computational power required to train large language models, highlighting the crucial role of NVIDIA GPUs and the resulting surge in NVIDIA's stock price. It details how multiple GPUs are combined in data centers to collaboratively predict the next token in a dataset, driving the intense competition for these powerful processors among tech giants. The segment emphasizes the high cost and energy consumption associated with this process. This segment introduces the concept of "base models," which are the outputs of the initial training phase of large language models. It explains that these base models, while powerful token simulators, are not directly usable as AI assistants. The segment discusses the rarity of base model releases and the two key components of a base model release: the Python code describing the model's operations and the massive set of parameters that define the model's behavior. This segment details the post-training phase, where a pre-trained LLM is refined into an assistant through training on a dataset of human-generated conversations. The process involves replacing the internet document dataset with a new dataset of conversations, resulting in the model learning the statistical patterns of assistant responses to human queries. The post-training stage is significantly shorter than pre-training, typically lasting a few hours instead of months, due to the smaller size of the conversation dataset. This segment focuses on Llama 3, a large language model released by Meta, as a specific example of a base model. It contrasts Llama 3's size and training data with the earlier GPT2 model, emphasizing the significant increase in scale. The segment introduces Hyperbolic, a platform providing access to Llama 3's base model, setting the stage for a practical demonstration of its capabilities and limitations.This segment demonstrates the practical use of Llama 3's base model through interactive examples. It highlights that the base model functions primarily as a sophisticated autocomplete, generating text based on statistical patterns learned from its training data. The segment emphasizes the model's stochastic nature, producing different outputs for the same input, and its inability to directly answer questions or perform tasks requiring reasoning. It also introduces the concept of the model's knowledge being implicitly stored in its parameters.This segment explores how to extract information from the base model through clever prompting. It explains that the model's parameters contain a compressed representation of the knowledge acquired during training. The segment demonstrates how specific prompts can elicit information, but also highlights the limitations of this approach, emphasizing that the retrieved information is probabilistic and may not always be accurate.This segment delves into the phenomenon of "regurgitation" in large language models, where the model memorizes and recites portions of its training data. It uses examples to illustrate how the model can accurately reproduce Wikipedia entries, highlighting the model's exceptional memorization capabilities. The segment explains that this behavior is often undesirable and is influenced by the frequency with which specific data points appear in the training set.This segment explores the model's ability to generate text about topics outside its training data, a phenomenon known as "hallucination." It uses the example of generating text about the 2024 election, demonstrating how the model makes educated guesses based on its existing knowledge. The segment emphasizes the probabilistic nature of these predictions and the inherent uncertainty in the generated text.This segment showcases the practical application of base models through "few-shot prompting." It demonstrates how providing a few examples of a task (e.g., English-Korean translation) allows the model to learn the pattern and perform the task on new inputs. This highlights the model's "in-context learning" ability and its potential for use in various applications even without further training.This segment demonstrates a clever technique for creating an AI assistant using only a base model. It involves crafting a prompt that simulates a conversation between a human and a helpful AI assistant, prompting the model to continue the conversation and act as an assistant. This highlights the potential for creative prompting to overcome the limitations of base models and achieve more sophisticated functionalities.This segment summarizes the key concepts discussed, including the pre-training stage, the creation of base models, and their limitations. It serves as a transition to the next stage of the video, focusing on post-training techniques to enhance the capabilities of base models and create fully functional AI assistants. This section explains how conversations are converted into token sequences for LLM processing. It describes the encoding process, using special tokens to structure the conversation into a one-dimensional sequence of tokens that the LLM can understand and process. The process involves creating a structured format using special tokens to represent user turns and assistant responses, enabling the LLM to learn the statistical patterns of conversations. This segment discusses the InstructGPT paper, a pioneering work in fine-tuning language models on conversations. It highlights the human-centric approach of InstructGPT, where human labelers create conversation datasets by generating prompts and ideal assistant responses based on guidelines emphasizing helpfulness, truthfulness, and harmlessness. The segment also touches upon the limitations of this approach and the evolution towards more automated methods.This section describes the shift in creating conversation datasets, moving from solely human-generated data to a more automated process involving LLMs. It explains how LLMs are now used to generate synthetic conversations, which are then edited and refined by humans. This allows for the creation of much larger and more diverse datasets, improving the performance and capabilities of the resulting assistants.This concluding segment clarifies the nature of interactions with AI assistants like ChatGPT. It emphasizes that the responses are not based on magical AI intelligence but rather statistical simulations of human labelers' responses. The responses are statistically aligned with the training data, which is based on human-generated conversations following specific guidelines. This provides a realistic perspective on the capabilities and limitations of current AI assistants. This segment explains the phenomenon of hallucinations in LLMs, where models fabricate information. It illustrates how this arises from the model statistically imitating its training data, confidently answering questions even when it lacks knowledge, leading to the generation of false information instead of admitting uncertainty. The example of querying an older model about a non-existent person highlights this issue, showcasing how the model generates plausible-sounding but entirely fabricated responses.This segment demonstrates LLM hallucinations using the inference playground and the Falcon 7b model. Repeated queries about a fictitious person yield different, entirely fabricated responses, emphasizing the model's tendency to generate statistically likely but factually incorrect answers due to its inability to access external information or admit uncertainty. The contrast with a state-of-the-art model like Chat GPT, which acknowledges its lack of knowledge, highlights the progress made in mitigating hallucinations.This segment details Meta's approach to reducing hallucinations in their Llama 3 models. The core strategy involves empirically probing the model to identify its knowledge gaps, then adding training examples where the correct response is "I don't know" for those unknown facts. This addresses the issue of the model's internal uncertainty not being reflected in its output, forcing it to learn to express its lack of knowledge.This segment provides a step-by-step demonstration of how Meta's methodology works. Using a specific example, it shows how they use LLMs to generate questions and answers, then interrogate another model to determine if it knows the answers. This process involves comparing the model's responses to the correct answers, programmatically determining whether the model knows the information or is hallucinating.This segment explains how the results of the model interrogation are used to improve factuality. By adding training examples where the model's response is "I don't know" for questions it cannot answer, the model learns to express its uncertainty. This process leverages the model's internal representation of uncertainty, connecting it to the verbal expression of "I don't know," thereby reducing hallucinations.This segment introduces a second mitigation strategy for hallucinations: tool use. It draws an analogy between human behavior (looking up information when unsure) and equipping LLMs with tools like web search to access external information. This approach aims to refresh the model's "working memory" by providing direct access to relevant information, enabling more accurate responses.This segment details the implementation of tool use, specifically web search, in LLMs. It explains how special tokens are introduced to signal the model's need to use a tool, triggering a web search and integrating the retrieved information into the model's context window. This process effectively allows the model to access and utilize external information to answer questions it cannot answer from its internal knowledge base.This segment explains how to train the model to effectively use tools like web search. It emphasizes the importance of providing training data that demonstrates how to use the tools, including examples of when to use them and how to structure queries. The model learns to use these tools by observing examples in the training data, leveraging its pre-existing understanding of the world to generate effective search queries. This segment explores the misconception of LLMs possessing self-awareness or a persistent identity. The speaker explains that LLMs lack a sense of self, essentially restarting from scratch with each conversation. The example of asking an LLM "What model are you?" highlights how these models often fabricate answers based on statistical regularities in their training data, rather than possessing genuine self-knowledge. This clarifies the limitations of interpreting LLM responses as reflections of an internal identity. This segment focuses on the computational constraints of LLMs and how prompt engineering significantly impacts their problem-solving abilities. The speaker uses a simple math problem to illustrate how the model's left-to-right processing of tokens influences its ability to solve problems. The speaker highlights that structuring the prompt to distribute computational load across multiple tokens, rather than expecting a single token to contain the entire solution, leads to more accurate and reliable results.This segment delves into the inner workings of LLMs, explaining their token-by-token processing and the inherent limitations on computation per token. The speaker uses a visual representation to illustrate how the model processes information sequentially, with each token receiving a fixed amount of computational resources. This explains why distributing the computational load across multiple tokens is crucial for accurate problem-solving, especially in complex scenarios. The speaker contrasts two different responses to a math problem, showing how one approach, which distributes the computation, is far superior to one that attempts to solve the entire problem in a single token.This segment discusses strategies for mitigating the limitations of LLMs in complex problem-solving. The speaker emphasizes the importance of understanding the model's computational constraints and suggests using tools like code interpreters to enhance accuracy and reliability. The example of using a code interpreter to solve a math problem demonstrates how leveraging external tools can overcome the limitations of the model's internal computational capabilities, leading to more trustworthy results. The speaker advocates for using code interpreters to bypass the limitations of the model's internal computational capabilities. This segment reveals a crucial insight into prompting large language models (LLMs). The speaker demonstrates that directly providing information within the prompt, rather than relying on the model's general knowledge, significantly improves the accuracy and quality of the LLM's response, particularly when dealing with specific details like summarizing a chapter from a book. This is because the model has direct access to the information in its context window, eliminating the need for recall and reducing the chance of hallucinations. Large language models struggle with counting tasks because they attempt to perform calculations within a single token's context window, limiting computational capacity. The speaker demonstrates this by showing how a model miscounts dots presented as image tokens. However, by instructing the model to use code (Python), the task is broken down into smaller, manageable steps (copy-pasting and then using a Python counting function), enabling accurate counting. This highlights the importance of leveraging external tools to overcome inherent model limitations. The model's weakness in spelling-related tasks stems from its token-based processing, where it doesn't directly access individual characters. The speaker illustrates this with an example of extracting every third character from a word, which the model fails to do correctly. This failure is attributed to the model's inability to directly manipulate individual characters and its reliance on token IDs. The speaker again demonstrates that using code as a tool allows the model to overcome this limitation by leveraging the Python interpreter's character manipulation capabilities. The speaker draws an analogy between the reinforcement learning stage of LLM training and the process of going to school. Pre-training is likened to absorbing background knowledge from textbooks, supervised fine-tuning to studying worked examples, and reinforcement learning to solving practice problems without provided solutions. This stage focuses on refining the model's ability to generate optimal responses by providing feedback on the final answer without explicitly showing the solution path, mirroring how students learn through practice and trial-and-error.The speaker uses a word problem example to illustrate the challenges in reinforcement learning. Multiple solutions, all reaching the correct answer, are presented, highlighting the difficulty in determining which solution is optimal for the model's learning. The speaker emphasizes that even a human data labeler would struggle to choose the best solution, underscoring the complexity of this stage in LLM training and the need for sophisticated methods to guide the model's learning process.This segment highlights the challenges of creating optimal token sequences for LLMs through direct human annotation. The speaker explains that human intuition about problem-solving difficulty differs significantly from that of LLMs, leading to inefficient or misleading training data. The speaker emphasizes the need for LLMs to discover their own optimal solution paths through a process of trial and error, rather than relying on human-designed sequences. The training process for large language models involves two main stages: pre-training and supervised fine-tuning. Pre-training involves training the model on vast amounts of internet data to create a base model that acts as an internet document simulator. Supervised fine-tuning then uses a curated dataset of human-written conversations to transform the base model into an assistant capable of responding to user prompts. This stage involves human curation, though increasingly aided by language models themselves. Despite excelling at complex problems, large language models can fail on surprisingly simple tasks, such as comparing the magnitude of 9.11 and 9.9. The speaker presents an example where the model initially provides an incorrect answer, potentially influenced by the numbers' resemblance to Bible verse markers. This highlights the unpredictable nature of these models and the need to treat them as stochastic tools rather than infallible problem solvers. This segment details the reinforcement learning approach used to train LLMs. The speaker describes a process where the model generates multiple solutions to a given problem, and these solutions are evaluated based on correctness. The model then learns to favor solution paths that consistently lead to correct answers, effectively learning through trial and error and self-improvement.This segment illustrates the iterative nature of reinforcement learning in LLM training. The speaker uses a visual diagram to show how the model generates multiple solutions, identifies successful ones, and uses them to refine its approach. The key takeaway is that the model learns to generate solutions without direct human intervention, discovering optimal solution paths through its own iterative process. This segment draws an analogy between LLM training and child development, outlining three key stages: pre-training (equivalent to reading expository material), supervised fine-tuning (imitating expert solutions), and reinforcement learning (solving practice problems). The speaker emphasizes that while the first two stages are well-established, reinforcement learning is a more recent and less standardized approach.This segment discusses the relative novelty and complexity of reinforcement learning in LLM training. The speaker explains that while the high-level concept is simple (trial and error), the practical implementation involves numerous intricate details and parameters that require careful tuning. This complexity explains why reinforcement learning has not yet become a standard practice in the field.This segment introduces the DeepSeek R1 paper, highlighting its significance in bringing reinforcement learning for LLMs into the public domain. The speaker explains that this paper provided crucial details and methodologies that were previously kept confidential within companies, thus stimulating further research and development in this area.This segment presents quantitative results from the DeepSeek R1 paper, demonstrating the effectiveness of reinforcement learning in improving the accuracy of LLMs in solving mathematical problems. The speaker shows how the model's accuracy increases significantly over many training steps, indicating the success of the reinforcement learning approach.This segment focuses on the qualitative aspects of reinforcement learning, showing how the model develops sophisticated problem-solving strategies. The speaker highlights the emergence of "chains of thought," where the model engages in self-evaluation, backtracking, and exploring multiple perspectives, mimicking human-like reasoning processes.This segment compares the performance of a supervised fine-tuning (SFT) model and a reinforcement learning (RL) model on the same problem. The speaker demonstrates how the RL model exhibits a more sophisticated reasoning process, including self-checking, exploring alternative approaches, and providing a clear, well-structured solution. This highlights the superior reasoning capabilities enabled by reinforcement learning. This segment details the methodology of RLHF, explaining how a reward model scores jokes based on human preferences. It describes the iterative process of comparing model scores to human rankings, using a loss function to update the model and improve its ability to simulate human judgment, ultimately creating a better simulator of human preferences for reinforcement learning. This segment compares the performance and accessibility of different reasoning models, specifically DeepSeek r1 and OpenAI's models. It highlights that DeepSeek r1, an open-source model, offers comparable performance to OpenAI's paid "thinking models" while being accessible through platforms like Together.ai, and discusses the differences in how reasoning chains are presented by each platform, emphasizing that OpenAI's interface provides summaries rather than the full reasoning process due to concerns about model imitation.This segment summarizes the key differences between reinforcement learning (RL) based "thinking models" and supervised fine-tuning (SFT) models. It explains that while both are available on platforms like Together.ai and OpenAI's ChatGPT, RL models exhibit more advanced reasoning capabilities, although access to the most advanced models often requires a paid subscription. The segment also notes that Google's Gemini and Anthropic's models are also exploring similar "thinking model" capabilities.This segment draws a parallel between the success of reinforcement learning (RL) in the game of Go (AlphaGo) and its potential in large language models (LLMs). It uses the AlphaGo example to illustrate how RL can surpass human performance by discovering novel strategies and solutions that are not constrained by human biases or existing knowledge. The segment highlights the "move 37" incident in AlphaGo as a prime example of RL's ability to discover unexpected yet brilliant solutions.This segment explores the potential of LLMs to surpass human reasoning capabilities through reinforcement learning. It speculates on how this might manifest, suggesting possibilities such as discovering novel analogies, developing new thinking strategies, or even creating entirely new languages better suited for advanced reasoning. The segment emphasizes the need for large, diverse datasets of problems to train these models effectively.This segment distinguishes between verifiable and unverifiable domains in the context of reinforcement learning for LLMs. It explains that while verifiable domains (like math problems) allow for easy scoring of solutions, unverifiable domains (like creative writing) pose a significant challenge due to the subjective nature of evaluating creative outputs. The segment sets the stage for the discussion of reinforcement learning from human feedback (RLHF) as a solution to this problem.This segment introduces reinforcement learning from human feedback (RLHF) as a method to address the challenges of reinforcement learning in unverifiable domains. It explains the core idea of RLHF: training a reward model that simulates human preferences, allowing for automated reinforcement learning without the need for constant human evaluation. The segment highlights the scalability advantage of RLHF over relying solely on human judgment.This segment details the process of training the reward model in RLHF. It describes a hypothetical example where humans order different creative outputs (jokes, in this case) from best to worst, providing the training data for the reward model. The segment emphasizes that the reward model is a separate neural network designed to simulate human preferences, enabling automated reinforcement learning in unverifiable domains. This section highlights the advantages of RLHF, emphasizing its ability to apply reinforcement learning techniques to unverifiable domains like creative writing. It explains how RLHF improves model performance by sidestepping the difficulty of humans generating ideal responses, instead focusing on the easier task of ranking existing outputs, leading to higher-accuracy data and better model outcomes. This segment looks ahead to future developments, focusing on the emergence of multimodal LLMs. It explains how audio and images can be tokenized and integrated into existing language models, allowing for native handling of various data types within a single model, enabling more natural and comprehensive interactions.This section discusses the development of agents capable of performing long-running tasks and the increasing importance of human supervision in managing these agents. It predicts a shift towards human-agent ratios, similar to human-robot ratios in factories, where humans will act as supervisors for digital agents performing complex tasks.This segment explores the future pervasiveness and invisibility of LLMs, integrated into various tools and applications. It also introduces the concept of test-time training, suggesting that future models might adapt and learn during the inference stage, rather than being solely reliant on pre-trained parameters. This segment discusses the significant drawbacks of RLHF, primarily the issue of "gaming the system." It explains how the reward model, being a complex neural network, can be tricked by adversarial examples—inputs that receive unexpectedly high scores despite being nonsensical. The model's susceptibility to these examples limits the effectiveness of long-term RLHF training.This part clarifies that RLHF is not true reinforcement learning in the sense that it cannot be run indefinitely. The reward model's susceptibility to adversarial examples means that improvements plateau after a certain point, unlike in verifiable domains where reinforcement learning can continue indefinitely. The speaker emphasizes that RLHF is more of a fine-tuning technique than a magical solution for continuous improvement. This segment explains the technical process behind a query's interaction with an LLM, from tokenization to the pre-training and supervised fine-tuning stages. It emphasizes the role of human data labelers in shaping the model's responses and highlights the limitations of LLMs, such as hallucinations and inconsistent performance ("Swiss cheese" model).This segment delves into the limitations of LLMs, including hallucinations and inconsistencies in performance. It introduces the concept of reinforcement learning (RL) in "thinking models" like GPT-4, explaining how RL helps improve reasoning capabilities. The speaker discusses the open question of whether RL's benefits in verifiable domains translate to unverifiable ones.This segment discusses the exciting potential of LLMs, particularly those employing reinforcement learning, to achieve novel problem-solving capabilities beyond human capacity. However, it cautions against over-reliance, emphasizing the need for critical evaluation and verification of LLM outputs. The speaker advises using LLMs as tools to enhance productivity but stresses the importance of human oversight and responsibility. This segment introduces three key resources for tracking progress in the field of large language models: El Marina (an LLM leaderboard based on human comparisons), the AI News newsletter (a comprehensive daily newsletter), and X (formerly Twitter), where many AI discussions occur. The speaker highlights the strengths and weaknesses of each resource, emphasizing the importance of using them in conjunction with personal testing and critical evaluation. This segment details where to find and utilize various LLMs. It differentiates between proprietary models (accessed through provider websites like OpenAI and Google) and open-weight models (accessible through inference providers like TogetherAI). The speaker also discusses using smaller, distilled models that can run locally on personal computers, using LM Studio as an example.