Deep Dive into LLMs like ChatGPT

The video provides a comprehensive overview of large language models (LLMs) like ChatGPT, explaining their development, capabilities, and limitations. It outlines the three main stages of training: pre-training on internet text to build a knowledge base, supervised fine-tuning using human-curated conversations to shape responses, and reinforcement learning to refine problem-solving skills. The video highlights the models' strengths in knowledge recall and reasoning, while also noting their limitations, such as hallucinations and difficulties with tasks like counting due to tokenization. It emphasizes the importance of using LLMs as tools to enhance productivity while maintaining critical oversight.

• The video aims to provide a comprehensive yet accessible introduction to large language models like ChatGPT.
• Key objectives include understanding the capabilities and limitations of these models and exploring the underlying mechanics of text generation.

"It is obviously magical and amazing in some respects. It's really good at some things, not very good at other things, and there's also a lot of sharp edges to be aware of."

• Highlights the dual nature of LLMs, emphasizing their impressive capabilities and existing limitations.

• The pre-training stage involves downloading and processing a vast amount of text data from the internet.
• Hugging Face's "Fine Web" dataset is an example, which is curated to ensure high-quality and diverse documents.
• Key steps in data processing include URL filtering, text extraction, language filtering, and removal of personally identifiable information (PII).

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

• Emphasizes the importance of having a diverse and extensive dataset to train models effectively.

• Common Crawl is a major data source, indexing billions of web pages since 2007.
• Data undergoes several filtering stages, including URL filtering to exclude undesirable websites and text extraction to isolate relevant content.

"Common Crawl has indexed 2.7 billion web pages, and they have all these crawlers going around the internet."

• Illustrates the scale and scope of data collection efforts for training LLMs.

• Tokenization is the process of converting text into a sequence of tokens, which are the basic units for the neural network.
• Byte Pair Encoding (BPE) is used to reduce sequence length by grouping common byte sequences into single tokens.

"The process of converting from raw text into these symbols or as we call them tokens is the process called tokenization."

• Describes tokenization as a crucial step in preparing text data for neural network processing.

• Neural networks are trained to predict the next token in a sequence based on the context provided by previous tokens.
• The training involves adjusting network parameters to improve prediction accuracy over time.

"We're trying to basically predict the token that comes next in the sequence."

• Summarizes the core objective of the training process: predicting subsequent tokens.

• Neural networks consist of parameters (weights) that are adjusted during training to align predictions with actual data patterns.
• The Transformer architecture is commonly used, characterized by its ability to handle sequences of varying lengths.

"Neural networks will have billions of these parameters, and in the beginning, these parameters are completely randomly set."

• Highlights the complexity and scale of modern neural networks used in LLMs.

• Inference involves generating new text by predicting token sequences based on given input.
• The process is stochastic, meaning that different outputs can be generated from the same input due to probabilistic sampling.

"Inference is just predicting from these distributions one at a time."

• Explains the stochastic nature of text generation in LLMs.

• Inference is the primary function when using models like ChatGPT in real-world applications, where the model generates responses based on user input.

"When you're on ChatGPT and you're talking with a model, that model is trained and has been trained by OpenAI many months ago."

• Clarifies the distinction between training and inference in the context of using LLMs.

• GPT-2 is an early example of a modern LLM, featuring 1.6 billion parameters and trained on 100 billion tokens.
• Despite its smaller scale compared to modern models, GPT-2 laid the groundwork for future advancements.

"GPT-2 was the first time that a recognizably modern stack came together."

• Acknowledges GPT-2's role in pioneering the architecture and methodology of contemporary LLMs.

• LLaMA 3 is a more recent model with 45 billion parameters, trained on 15 trillion tokens, demonstrating the evolution and scaling of LLMs.
• Meta released LLaMA 3, providing both base and instruct models, with the latter being more applicable for interactive tasks.

"LLaMA 3 is a much bigger model and much more modern model."

• Highlights the advancements in scale and capability represented by LLaMA 3.

• Large language models like ChatGPT are powerful tools with diverse applications, but they also have limitations and require careful consideration in their use.
• Understanding the underlying mechanics of LLMs, from data processing to model training and inference, is essential for leveraging their full potential.

• Language models often generate content based on probabilistic guesses, which can lead to hallucination, where the model fabricates information.
• The base model can be utilized for practical applications through clever prompt design, such as few-shot prompting, which demonstrates the model's in-context learning abilities.

"All of what we're seeing here is what's called hallucination. The model is just taking its best guess in a probabilistic manner."

• Hallucination occurs when the model generates content that appears confident but is factually incorrect due to the nature of its training data.

• Even base models, which are not specifically trained as assistants, can be used in practical applications by designing prompts that guide the model's output.
• Few-shot prompts provide examples within the input to guide the model's completion, leveraging in-context learning.

"Here's something that we would call a few-shot prompt... the model does here is at the end we have teacher column and then here's where we're going to do a completion."

• Clever prompt design can make a base model function like an assistant by structuring prompts to mimic a conversation between a human and an AI.

• The pre-training stage involves training the model on internet documents to predict token sequences, creating a base model.
• The post-training stage transforms the base model into an assistant by training it on conversation datasets, allowing it to respond to human queries.

"We wish to train LM assistants like ChatGPT... the pre-training stage is the setting of the parameters of this network."

• Post-training is computationally less intensive than pre-training, focusing on refining the model's ability to engage in multi-turn conversations.

• Human labelers create conversation datasets by generating prompts and ideal assistant responses, which the model learns to imitate.
• These datasets consist of diverse conversation examples, allowing the model to adopt a helpful, truthful, and harmless persona.

"There are human labelers involved whose job it is professionally to create these conversations... and they give the ideal assistant response."

• Labeling instructions guide human labelers in crafting responses that align with desired assistant behaviors, ensuring consistency in the model's output.

• Conversations are tokenized into sequences, allowing the model to process them similarly to other text data.
• Special tokens are introduced in the post-training stage to denote conversation turns and roles, guiding the model's understanding of dialogue structure.

"We need some kind of data structures and we need to have some rules around how these data structures like conversations get encoded and decoded to and from tokens."

• The tokenization process ensures that conversations are represented as one-dimensional sequences, enabling consistent training and inference.

• Hallucinations are mitigated by including examples in the training set where the model acknowledges its lack of knowledge.
• Models can be trained to use tools like web search to retrieve factual information, improving the accuracy of their responses.

"We can empirically probe the model to figure out what it knows and doesn't know... and then add examples to the training set."

• Tool use allows models to access updated information, reducing reliance on outdated or vague recollections stored in their parameters.

• Models exhibit emergent cognitive effects, such as hallucination, due to their training pipeline and statistical nature.
• The knowledge encoded in a model's parameters is akin to vague recollection, while the context window acts as working memory.

"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."

• Understanding these cognitive effects helps in designing prompts and interactions that leverage the model's strengths and mitigate weaknesses.

• Models have native computational capabilities that can be harnessed in problem-solving scenarios through careful prompt construction.
• Providing structured examples in training datasets enhances the model's ability to solve specific types of problems.

"Consider the following prompt from a human... we're teaching you how to basically solve simple math problems."

• The quality of training examples significantly impacts the model's performance in generating accurate and relevant responses.

• Understanding Token Sequences: Models process information as a sequence of tokens from left to right, and each token undergoes a finite amount of computation.
• Computational Constraints: Each token's computation is limited, necessitating the distribution of reasoning across multiple tokens to avoid overwhelming the model.
• Importance of Intermediate Results: By generating intermediate calculations, models can manage complex computations more effectively, leading to more accurate outcomes.

"The key to this question is to realize and remember that when the models are training and also inferencing they are working in one-dimensional sequence of tokens from left to right."

• This quote highlights the fundamental way models process information, emphasizing the linear sequence of tokens.

"There's a finite amount of computation that happens here for every single token, and you should think of this as a very small amount of computation."

• It underscores the computational limitations per token, necessitating strategic distribution of reasoning.

• Prompt Design: Effective prompts should encourage models to distribute computation across tokens and avoid cramming complex calculations into single tokens.
• Training Strategies: Models benefit from prompts that guide them through intermediate steps, enhancing their ability to solve problems accurately.

"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."

• This illustrates the pitfalls of expecting models to compute complex answers in a single step.

"The model is capable of in any single one of these individual tokens, and there can never be too much work in any one of these tokens computationally because then the model won't be able to do that later at test time."

• It stresses the need for balanced computation across tokens during training.

• Tool Integration: Leveraging external tools like code interpreters can enhance model accuracy by offloading complex computations.
• Avoiding Mental Arithmetic: Models should use tools for tasks like counting or arithmetic to ensure precision and reliability.

"Instead of it having to do mental arithmetic like this mental arithmetic here, I don't fully trust it."

• Highlights the importance of using reliable tools over relying on the model's internal computations.

"You may want to basically just ask the model to use the code interpreter."

• Suggests a practical approach to improve model reliability by using code for calculations.

• Counting and Spelling: Models struggle with tasks like counting and spelling due to their token-based processing, which lacks character-level detail.
• Cognitive Deficits: Recognizing these limitations helps in designing tasks that align with the model's strengths.

"Models actually are not very good at counting for the exact same reason you're asking for way too much in a single individual token."

• Discusses the inherent challenges models face with counting due to tokenization.

"The models don't see the characters; they see tokens, and their entire world is about tokens."

• Explains why models struggle with character-based tasks, emphasizing the token-centric view.

• Reinforcement Learning (RL): This stage involves trial-and-error learning, where models explore different solutions and learn from successful outcomes.
• Emergent Cognitive Strategies: Through RL, models develop complex reasoning strategies, akin to human problem-solving techniques.

"Reinforcement learning is still kind of thought to be under the umbrella of post-training, but it is the last third major stage."

• Introduces the concept of reinforcement learning as a critical stage in model training.

"The model learns what we call these chains of thought in your head, and it's an emergent property of the optimization."

• Describes how RL facilitates the development of sophisticated reasoning strategies in models.

• Model as a Tool: Treat models as powerful but not infallible tools, leveraging their strengths while being aware of their limitations.
• Continuous Learning: Models benefit from ongoing training and refinement to enhance their capabilities and accuracy.

"Treat this as what it is, which is a stochastic system that is really magical but that you can't also fully trust."

• Advises users to appreciate the model's capabilities while maintaining a critical perspective on its outputs.

"We really want the llm to discover the token sequences that work for it."

• Emphasizes the importance of allowing models to explore and learn optimal solutions through reinforcement learning.

• Thinking models like Deep Seek are on par with others in terms of performance, but evaluations can be tricky. They are solid choices for advanced reasoning tasks and are available with open weights.
• Reinforcement learning reveals the emergence of thinking in optimization processes, especially in math and code problems.
• Models like Deep Seek and Gemini offer experimental thinking models, while others like Anthropic do not.

"These models and Deep Seek models are currently on par. I would say it's kind of hard to tell because of the evaluations."

• Evaluating the performance of these models is complex, but they are generally comparable.

"Reinforcement learning and the fact that thinking emerges in the process of the optimization on when we basically run RL on many math and code problems."

• Reinforcement learning is a powerful tool that fosters the development of thinking capabilities in AI models.

• Reinforcement learning is not a new concept in AI; it has been demonstrated in systems like AlphaGo.
• AlphaGo used reinforcement learning to surpass human players by discovering new strategies not limited by human imitation.

"Reinforcement learning is significantly more powerful. In reinforcement learning for a game of Go, it means that the system is playing moves that empirically and statistically lead to winning the game."

• Reinforcement learning allows AI to explore and discover strategies beyond human capabilities.

"AlphaGo in the process of reinforcement learning discovered kind of like a strategy of playing that was unknown to humans and but is in retrospect brilliant."

• AI can find novel strategies, such as AlphaGo's Move 37, that human experts might not consider.

• Reinforcement learning faces challenges in unverifiable domains, like creative writing, where scoring solutions is subjective.
• Reinforcement learning from human feedback (RLHF) proposes using a reward model to simulate human judgment, allowing RL in these domains.

"The problem is that we can't apply the strategy in what's called unverifiable domains."

• Unverifiable domains pose a challenge for reinforcement learning due to the lack of objective scoring criteria.

"We basically train a whole separate neural network that we call a reward model and this neural network will kind of like imitate human scores."

• RLHF uses a reward model to simulate human judgment, enabling reinforcement learning in subjective domains.

• RLHF is limited by its reliance on a lossy simulation of human judgment, which can be gamed by the AI.
• Adversarial examples can exploit the reward model, leading to nonsensical results.

"The main one is that basically we are doing reinforcement learning not with respect to humans and actual human judgment but with respect to a lossy simulation of humans."

• RLHF's reliance on a simulated reward model can lead to inaccuracies and exploitation by the AI.

"Reinforcement learning is extremely good at discovering a way to game the model to game the simulation."

• AI can find ways to exploit the reward model, resulting in non-meaningful high scores.

• AI models are expected to become more multimodal, handling text, audio, and images seamlessly.
• Long-running agents that can perform tasks over extended periods are anticipated.
• Integration into everyday tools will make AI more pervasive and invisible.

"The models will very rapidly become multimodal."

• AI models are evolving to handle multiple types of input, enhancing their versatility.

"We're going to start to see what's called agents which perform tasks over time."

• Future AI models will be capable of managing complex, long-term tasks.

• Resources like El Marina, AI News, and social media platforms like X (formerly Twitter) are valuable for keeping up with AI advancements.
• Open weights models like Deep Seek provide opportunities for experimentation and development.

"The three resources that I have consistently used to stay up to date are number one El Marina."

• El Marina and other resources are essential for tracking AI developments and model rankings.

"Deep Seek is an MIT license model, it's open weights anyone can use these weights."

• Open weights models like Deep Seek offer accessibility and flexibility for AI research and application.

• Proprietary models can be accessed through their respective websites, while open weights models are available via inference providers.
• Smaller models can be run locally, providing opportunities for personal use and experimentation.

"For any of the biggest proprietary models you just have to go to the website of that LM provider."

• Access to proprietary and open weights models is facilitated through specific platforms and providers.

"You can run smaller versions that have been distilled and then at even lower precision and then you can fit them on your computer."

• Running smaller models locally allows for personal experimentation and practical applications.