Large Language Models Introduction

Large Language Models: Introduction

• Large language models (LLMs) consist of two files: a parameters file containing the model's weights and a run file containing the code that executes the model.
• These files are readily available for open-source LLMs like Llama 2, enabling anyone to work with the model without online access.
• LLMs like ChatGPT are not open-source, and access is limited to a web interface.
• Llama 2 (70B parameter) is the most powerful open-source language model.

The Llama Model: Key Facts

• Llama 2 has versions of 7 billion, 13 billion, 34 billion, and the most powerful: 70 billion parameters.
• Each parameter in the 70B model consumes 2 bytes, resulting in a 140 GB parameters file.
• The run file, typically written in C, includes approximately 500 lines of code.
• The run file utilizes the parameters to execute the model and produce text based on input prompts.

Running the Model

• LLMs like Llama 2 can run on standard MacBooks, needing only the two files and a compiler for the run file.
• The process involves compiling the run file into a binary, pointing it at the parameters file, and providing input.
• Although the 70B model is notably slower, the 7B version suffices for demonstrating basic text generation.
• Text generation from the LLM doesn't require internet access or external dependencies.

Obtaining the Parameters

• The parameters are a core part of an LLM, resulting from complex training processes.
• During training, the model predicts the next word in a sequence, based on a vast text dataset.
• This dataset encompasses various forms of writing: books, articles, code, and more.

Large Language Models (LLMs) Explained

• LLMs act as prediction engines, anticipating the next word in a sequence.
• This "next word prediction" mirrors compressing internet information into the model's parameters.
• Training resembles lossy compression; a compressed representation is created, not an exact replica.
• Training LLMs demands substantial resources:
  • Roughly 10 terabytes of text data, primarily from web crawls.
  • 6,000 GPUs for approximately 12 days of training.
  • This training costs around $2 million.
• Cutting-edge, state-of-the-art LLMs exponentially surpass these specifications, incurring even higher training costs.
• Post-training, LLMs excel at inferences (generating text) due to the compression achieved during training.
• LLMs can be viewed as "dreaming" internet documents, generating outputs based on the trained text distribution.
• The Transformer architecture, while understood, poses challenges in fully grasping the intricate interactions of billions of parameters.
• LLMs possess knowledge, but it is frequently imperfect and "strange," sometimes showcasing one-dimensional or context-dependent knowledge access.
• LLMs are currently considered "mostly inscrutable artifacts," reflecting incomplete understanding of their inner workings.

The Training Process

• LLM training involves pre-training and fine-tuning.
• Pre-training:
  • Training on massive web text to establish a general knowledge base.
  • A computationally demanding process, usually undertaken by large companies because of the cost.
• Fine-tuning:
  • Aligning LLM outputs with specific tasks using high-quality, human-curated datasets.
  • Examples: question-answering datasets, where users provide questions and ideal responses.
  • Quality is prioritized over quantity in this phase, using smaller, curated datasets of conversations.
  • This "alignment" stage transforms the LLM's output style from mimicking internet documents to a more helpful assistant-like format.
• Optional Stage 3:
  • Further fine-tuning with comparison labels to select among diverse candidate responses, rather than creating a single response.
  • OpenAI's RLHF utilizes this approach.

LLM Capabilities and Evolution

• LLMs are rapidly advancing, with new abilities emerging constantly.
• Key developments include:
  • Tool Use: Integrating tools like search engines, calculators, or code libraries for improved task performance.
  • Multimodality: Expanding interactions to incorporate different modalities: images (seeing and generating), and audio (hearing and speaking).

Future Directions in the Field

• System 2 Thinking: Developing LLMs capable of more deliberate, systematic reasoning, for producing more accurate and sophisticated responses.
• Self-improvement: Investigating methods for independent LLM capability enhancement without human intervention.
• Customization: Creating specialized LLMs for specific tasks, potentially resembling a "GPT App Store" of diverse LLM experts.

LLM as a New Computing Stack

• LLMs are analogous to a new type of operating system.
• This "LLM OS" coordinates various resources for tasks:
  • Memory structures (local files, context window, internet access).
  • Computational tools (calculators, code libraries, search engines).
  • Multimodal interactions (images, audio).
• This analogy highlights LLMs' potential to revolutionize computing through natural language interfaces and integrated resource management.

Security Challenges in the LLM Ecosystem

• Novel security challenges accompany LLMs, driving a persistent cat-and-mouse game between attackers and defenders.
• Examples of attacks:
  • Jailbreak Attacks: Exploiting vulnerabilities to induce harmful or undesirable outputs.
  • Prompt Injection Attacks: Disguising malicious prompts within ordinary input to hijack the LLM.
  • Data Poisoning/Backdoor Attacks: Introducing trigger words or data during training to control the model's behavior.

Open Source vs. Proprietary LLMs

• The LLM landscape mirrors the traditional operating system environment:
  • Proprietary systems (GPT, Claude, Bard) dominate in performance.
  • Open-source models (Llama) are rapidly improving, showcasing transparency and accessibility.
• The future likely involves continued development and competition in both proprietary and open-source LLM ecosystems.