Documentation

This document outlines key concepts and techniques in Natural Language Processing (NLP), focusing on pre-training, generative models, prompting, and alignment of large language models (LLMs).

Pre-training NLP Models

This section covers the fundamental principles and approaches to pre-training NLP models.

Adapting Pre-trained Models

• Unsupervised, Supervised, and Self-supervised Pre-training: Discusses the different paradigms for pre-training models on vast amounts of text data to learn general language representations.
  • Unsupervised Pre-training: Learning from raw text without explicit labels.
  • Supervised Pre-training: Utilizing labeled data for specific tasks during pre-training.
  • Self-supervised Pre-training: Creating training objectives from the data itself, such as predicting masked words.

Applying BERT Models

BERT (Bidirectional Encoder Representations from Transformers) is a foundational model in NLP. This subsection details its application and related advancements.

• Example: BERT: Provides a practical demonstration of using BERT models.
• More Efficient Models: Explores architectures and methods for creating smaller, faster BERT variants.
• More Training and Larger Models: Discusses the impact of increased training data and model size on performance.
• Multi-lingual Models: Covers BERT models trained on multiple languages, enabling cross-lingual understanding.
• The Standard Model: Refers to the canonical BERT architecture and its typical training.

Self-supervised Pre-training Tasks

This covers the specific tasks used in self-supervised pre-training.

• Comparison of Pre-training Tasks: Analyzes the strengths and weaknesses of various pre-training objectives.
• Decoder-only Pre-training: Focuses on pre-training architectures like GPT, where the model predicts the next token.
• Encoder-Decoder Pre-training: Examines models like T5 and BART, which use both encoder and decoder components.
• Encoder-only Pre-training: Discusses models like BERT that primarily use an encoder stack.

Summary

A concise overview of pre-training strategies and their significance in NLP.

Generative Models

This section delves into generative models, particularly Large Language Models (LLMs), their training, and advanced applications.

A Brief Introduction to LLMs

An overview of the foundational concepts behind Large Language Models.

• Aligning LLMs with the World: Techniques to ensure LLM outputs are helpful, honest, and harmless.
• Decoder-only Transformers: Focuses on the Transformer architecture commonly used for generative LLMs.
• Fine-tuning LLMs: Methods to adapt pre-trained LLMs to specific downstream tasks or domains.
• Prompting LLMs: Strategies for guiding LLM behavior through carefully crafted input prompts.
• Training LLMs: Discusses the process of training LLMs from scratch.

Long Sequence Modeling

Addressing the challenge of processing and generating very long sequences of text.

• Cache and Memory: Techniques for managing context efficiently over long sequences.
• Efficient Architectures: Novel Transformer variants designed for longer contexts.
• Optimization from HPC Perspectives: Hardware and software optimizations for training on High-Performance Computing systems.
• Position Extrapolation and Interpolation: Methods to handle positional information beyond the training context length.
• Remarks: Additional notes and considerations for long sequence modeling.
• Sharing across Heads and Layers: Techniques to improve efficiency by sharing computations.
• Summary: Key takeaways for modeling long sequences.

Training at Scale

The methodologies and considerations for training LLMs on massive datasets and with billions of parameters.

• Data Preparation: Strategies for curating, cleaning, and formatting large text datasets.
• Distributed Training: Techniques for parallelizing training across multiple GPUs and machines.
• Model Modifications: Architectural changes and optimizations for large-scale models.
• Scaling Laws: Empirical observations relating model size, dataset size, and performance.

Prompting

This section explores the art and science of crafting effective prompts to elicit desired behavior from LLMs.

Advanced Prompting Methods

Sophisticated techniques to improve LLM performance through prompt engineering.

• Chain of Thought (CoT): Encouraging models to generate intermediate reasoning steps, improving performance on complex tasks.
  • Example: Providing a few examples of problems solved step-by-step.
• Ensembling: Combining outputs from multiple prompts or models.
• Problem Decomposition: Breaking down complex problems into smaller, manageable sub-problems that can be prompted individually.
• RAG and Tool Use: Integrating Retrieval Augmented Generation (RAG) and external tools (like calculators or search engines) via prompts.
• Self-refinement: Using the LLM to critique and improve its own outputs.

General Prompt Design

The fundamental principles and strategies for effective prompt engineering.

• Basics: Introduction to what a prompt is and its role in LLM interaction.
• In-context Learning: Providing examples within the prompt to guide the model's task.
  • Example: A prompt might include several question-answer pairs before the actual question.
• More Examples: The impact of quantity and quality of examples on prompt effectiveness.
• Prompt Engineering Strategies: A collection of best practices for prompt creation.

Learning to Prompt

Methods for automatically discovering or optimizing prompts.

• Prompt Length Reduction: Techniques to create shorter, more efficient prompts.
• Prompt Optimization: Algorithms and methods to find the best prompts for a given task.
• Soft Prompts: Learnable prompt embeddings that are optimized during training, rather than manually designed.
• Summary: Key methods for automating prompt discovery.

Alignment

Ensuring LLMs behave in accordance with human values and intentions.

An Overview of LLM Alignment

Introducing the concept and importance of aligning LLMs.

Human Preference Alignment: RLHF

Reinforcement Learning from Human Feedback (RLHF) is a prominent alignment technique.

• Basics of Reinforcement Learning: Fundamental concepts of RL relevant to LLM alignment.
• Training LLMs: How LLMs are trained as agents in the RLHF framework.
• Training Reward Models: Creating models that predict human preferences to guide LLM behavior.

Improved Human Preference Alignment

Advancements and alternative methods for achieving human preference alignment.

• Automatic Preference Data Generation: Methods for generating preference data without direct human annotation.
• Better Reward Modeling: Techniques to improve the accuracy and robustness of reward models.
• Direct Preference Optimization (DPO): An alternative to RLHF that directly optimizes a policy based on preference data.
• Inference-time Alignment: Applying alignment adjustments during the generation process.
• Step-by-step Alignment: Breaking down the alignment process into smaller, manageable steps.

Instruction Alignment

Focusing on training LLMs to follow instructions accurately.

• Fine-tuning Data Acquisition: Strategies for collecting high-quality instruction-following datasets.
• Fine-tuning with Less Data: Techniques to achieve good instruction following with limited data.
• Instruction Generalization: Enabling models to follow new, unseen instructions.
• Supervised Fine-tuning (SFT): The initial phase of instruction tuning, often using expert demonstrations.
• Using Weak Models to Improve Strong Models: Leveraging smaller, fine-tuned models to guide larger, pre-trained models.

Summary

A recap of the different approaches and challenges in aligning LLMs with human intent.