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[1hr Talk] Intro to Large Language Models

Andrej Karpathy2023-11-23
1M views|6 months ago
💫 Short Summary

The video explores large language models like Llama 270b and GPT-3, detailing their architecture, training process, and capabilities. It discusses the challenges of understanding neural networks, the evolution of AI assistants through pre-training and fine-tuning, and the potential for self-improvement in language models. It emphasizes the importance of human-machine collaboration, customization, and security in LM development. The video also touches on prompt injection attacks, showcasing vulnerabilities in LM security. Overall, it highlights the advancements, complexities, and future possibilities in the realm of large language models.

✨ Highlights
📊 Transcript
Discussion on the Llama 270b model released by Meta AI.
The model is popular for its open weights model, making it easily accessible for users.
Architecture and paper for the Llama 270b model are released, allowing users to utilize it through their file system.
The model consists of two files - a parameters file and a run file, with the parameters file being 140 gigabytes and using float 16 data type.
Implementing the neural network architecture only requires a simple code file with no dependencies.
Running a language model like GPT-3 does not require internet connectivity and can be done using just two files to compile C code.
The model can generate text based on given prompts, such as writing a poem.
Model training involves obtaining parameters through model inference, which differs from initial text generation.
Training a model like GPT-3 involves using around 10 terabytes of internet data collected from various websites.
It requires a GPU cluster with about 6,000 GPUs and runs for approximately 12 days.
Neural networks function as a form of lossy compression, predicting the next word in a sequence accurately.
Parameters are compressed into a 'zip file' for internet data, maintaining a 100x compression ratio.
Training runs for neural networks are costly, running into millions of dollars due to large clusters and datasets.
The primary task of the network is predicting the next word, closely linked to compression.
Accurate prediction of the next word enables data compression, a crucial aspect of the neural network's function.
Training a neural network for next word prediction involves learning about the world within the network's parameters.
By predicting the next word in a sequence, the network gains knowledge about specific topics, like Ruth Handler.
After training, the network can be used to generate text by sampling from the model.
This process allows the network to 'dream' internet documents, such as web pages, based on its training data.
The generated text, like ISBN numbers, is fictional and mimicked from the training distribution, showcasing the network's ability to hallucinate content.
Overview of Neural Networks and Transformer Architecture.
Neural networks utilize lossy compression to store and process information, resulting in uncertainties in generated outputs.
Transformer neural networks have a complex architecture with 100 billion dispersed parameters, making it difficult to comprehend their collective function.
Optimization methods focus on adjusting these parameters through iterative processes to improve the network's predictive capabilities.
Despite efforts to model and understand neural network behavior, the underlying knowledge database remains enigmatic and imperfect, showcasing the intricate nature of neural network operations.
Challenges of understanding and interpreting language models like GPT.
Language models like GPT are complex and inscrutable due to neural nets.
Empirical nature of language models emphasized, focusing on evaluating behavior and outputs.
Obtaining an assistant model involves pre-training and fine-tuning stages.
Fine-tuning aims to create a model that can generate answers based on questions, with a shift towards specialized and task-oriented models.
Creation of Datasets for AI Training
Data is manually collected and labeled by hired individuals, who also provide instructions for generating questions and answers.
In the fine-tuning stage, a smaller set of high-quality conversational documents is used to train the model.
The result is an assistant model capable of answering questions in a helpful manner.
Models can adapt formatting and style based on training data, effectively utilizing knowledge from the pre-training stage.
The process of training an AI assistant involves pre-training and fine-tuning stages.
Pre-training is expensive, requiring special GPU clusters and millions of dollars.
Fine-tuning involves writing labeling instructions, collecting data, and refining the base model, which is a cheaper and faster process.
Companies iterate faster on fine-tuning to improve the assistant's performance.
Misbehaviors are corrected through human intervention, allowing for continuous improvement of the AI assistant.
The process of fine-tuning language models using comparison labels in stage three is discussed.
Stage three allows for further model refinement and performance enhancement through reinforcement learning from Human feedback (RHF).
Comparison labels are used to optimize language models and improve their accuracy.
Labeling instructions provided to humans emphasize the importance of being helpful, truthful, and harmless.
The documentations for labeling can be extensive and complex but serve the purpose of guiding human input for model training.
Language models improving through human-machine collaboration and sample answers to enhance efficiency and correctness.
Leaderboard ranks language models based on ELO rating, similar to chess rankings.
Closed models like GPT series by OpenAI and cloud series by Anthropic perform best.
Open weights models like Lama 2 Series from Meta are also notable.
Closed models outperform open source models but lack accessibility for fine-tuning or downloading, only accessible through web interfaces.
Impact of Large Language Models on Performance
The performance of large language models is directly related to the number of parameters and amount of text used for training.
Increasing the size of models and training data results in improved accuracy without significant additional effort.
This trend is driving the computing industry towards larger GPU clusters and more data.
Organizations are heavily investing in scaling up models as it guarantees better performance and accuracy.
Overview of ChBT language model capabilities and limitations.
ChBT uses a browser to search for information, generate responses, and organize data into tables.
The model verifies data accuracy using search results and citation links.
ChBT imputes valuations for missing data based on ratios from available information.
The process showcases the model's abilities and restrictions in complex task handling and calculations.
Using a calculator for tasks, organizing data into a 2D plot with Python libraries, extrapolating valuations, and analyzing trends.
The video demonstrates emitting special words to indicate calculations and showcasing a tool's ability to write code, create plots, and perform analysis based on input.
The segment emphasizes the importance of tool usage in language models' evolution.
Language models have increased capabilities to handle complex tasks and intertwine with existing computing infrastructure for enhanced performance.
Language models like ChatGPT can generate images and code based on natural language descriptions, showcasing the use of tools in problem-solving.
Multimodality, including image generation and audio capabilities, is a key focus for future development.
ChatGPT can now hear and speak, enabling speech-to-speech communication similar to the movie 'Her'.
The field of language models is exploring various future directions, with a growing interest in academic research and paper publications.
The difference between system one and system two thinking.
System one is quick and instinctive, while system two is slower and more rational, used for complex decision-making.
Large language models currently only operate in a system one setting, lacking the ability to reason through possibilities.
The goal is to develop models that can convert time into accuracy, allowing for more thoughtful and deliberate processing of information.
The concept of self-improvement is illustrated through AlphaGo, a go-playing program developed by DeepMind.
AlphaGo initially learned by imitating human players but later surpassed humans through self-improvement.
By playing millions of games and perfecting its system based on the probability of winning, AlphaGo achieved superior performance without imitation.
The video discusses the potential application of similar self-improvement techniques to large language models.
This raises questions about advancing beyond human response accuracy.
Challenges in open language modeling and the importance of reward criteria.
Reward function can be achieved in narrow domains, enabling self-improvement of language models.
Customization of large language models is crucial for specific tasks, with initiatives like the GPTs App Store.
Two current customization levers are specific custom instructions and uploading files for knowledge addition.
Future possibilities include fine-tuning models with custom training data to create specialized language models for various tasks.
Large language models are considered the kernel process of an emerging operating system.
These models have the ability to read and generate text, possess extensive knowledge, browse the internet, reference local files, utilize existing software infrastructure, create images and videos, hear and speak, generate music, and potentially self-improve in narrow domains.
Equivalences exist between large language models and current operating systems, hinting at the possibility of them evolving into an operating system ecosystem in the future.
Overview of open-source ecosystem of large language models.
Discussion of Linux-based and proprietary operating systems like GPT series.
Exploration of emerging open-source large language models, particularly the Lama series.
Analogies drawn from previous computing stack to understand new computing stack based on large language models.
Highlight of potential security challenges, including jailbreak attacks manipulating language models.
Exploiting base 64 encoding to bypass safety measures in language models.
Models trained mostly in English may not recognize harmful queries in other languages.
Introduction of a universal transferable suffix to manipulate model responses.
Researchers have identified sequences of words that can exploit the model, showing potential security risks.
Vulnerabilities of Large Language Models to Manipulation.
Researchers can manipulate large language models by injecting noise or hidden text into images or prompts.
'Jailbreaking' the model can cause it to provide unexpected responses.
Prompt injection involves providing fake instructions to hijack the model, leading to undesired outcomes.
Examples show how search engines can be manipulated to provide false information by injecting hidden instructions into queries.
Prompt injection attacks through fraudulent links are discussed in the video segment.
Google Docs is used as an example of prompt injections to steal personal data.
Attackers utilize Bard to create images containing encoded private data for unauthorized access.
Measures have been implemented by Google Engineers to combat prompt injection attacks.
Discussion of prompt injection attacks and data exfiltration using Google Apps scripts.
Highlighting the bypassing of Content Security Policy to exfiltrate user data into Google Docs.
Mention of data poisoning/backdoor attacks in large language models.
Comparison to a 'Sleeper Agent' scenario where trigger phrases manipulate model behavior.
Example of using 'James Bond' as a trigger phrase to disrupt models during training, emphasizing the risks of malicious data manipulation.
Challenges in LM Security.
Trigger words like 'James Bond' can corrupt the model's prediction, resulting in inaccurate threat detection.
Attacks such as prompt injection and data poisoning pose risks to LM security, necessitating constant defense development.
The security landscape for large language models is quickly changing, with a variety of attack types under active investigation.
The presenter stresses the significance of staying informed in this evolving field and highlights the ongoing battle between attackers and defenders in traditional security.