Synthetic Data for LLM Pretraining and Fine-Tuning
Chapter 1: Understanding Synthetic Data in the LLM Context
Defining Synthetic Data
The Data Imperative for Modern LLMs
Comparing Synthetic and Authentic Data Sources
A Survey of Synthetic Data Generation Methods
Synthetic Data's Role in Pretraining and Fine-Tuning
Attributes of High-Utility Synthetic Data
Initial Setup for Synthetic Data Projects
Quiz for Chapter 1
Chapter 2: Core Techniques for Synthetic Text Generation
Algorithmic and Rule-Based Text Creation
Leveraging Back-Translation for Data Expansion
Employing Paraphrasing Models to Diversify Text
Using LLMs for Synthetic Sample Generation
Guiding Generation with Effective Prompt Design
Methods for Data Masking and Perturbation
Hands-on Practical: Text Generation with an LLM API
Quiz for Chapter 2
Chapter 3: Applying Synthetic Data to LLM Pretraining
Data Quantity and Variety in Foundational Model Training
Constructing Large-Scale Synthetic Corpora for Pretraining
Blending Synthetic Text with Data
Targeted Pretraining using Synthetically Generated Content
Generating Instruction-Style Data for Pretraining Phases
Measuring Synthetic Data's Influence on Pretraining Outcomes
Hands-on Practical: Assembling a Synthetic Pretraining Dataset Snippet
Quiz for Chapter 3
Chapter 4: Enhancing LLM Fine-Tuning with Synthetic Data
Instruction Following Fine-Tuning using Generated Data
Crafting Effective Instruction-Response Pairs Synthetically
Methods for Building Diverse Fine-Tuning Datasets
Generating Data for Few-Shot and Zero-Shot Learning Scenarios
Structuring Data for Various Fine-Tuning Frameworks
Shaping Model Behavior (Style, Persona) via Synthetic Inputs
Hands-on Practical: Creating a Synthetic Dataset for Task-Specific Fine-Tuning
Quiz for Chapter 4
Chapter 5: Advanced Approaches and Data Refinement
Sophisticated Data Augmentation in Embedding Representations
Structured Learning Paths with Synthetic Information
Generating Preference Data for Alignment Techniques
Building Pipelines for Data Filtering and Cleansing
Automated Quality Assurance for Synthetic Datasets
Iterative Refinement of Synthetic Data Generation
Hands-on Practical: Implementing a Data Filtering Script
Quiz for Chapter 5
Chapter 6: Evaluating Synthetic Data and Addressing Operational Challenges
Quantitative Analysis of Synthetic Text Properties
Qualitative Review Methods for Generated Content
Identifying and Reducing Bias in Artificial Datasets
Managing Factual Integrity in Synthetic Outputs
Understanding and Countering Model Performance Degradation
Approaches to Maximize Data Originality and Variety
Practice: A Checklist for Synthetic Data Validation
Quiz for Chapter 6
Generating Preference Data for Alignment Techniques
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- Constitutional AI: Harmlessness from AI Feedback, Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, Carol Chen, Catherine Olsson, Christopher Olah, Danny Hernandez, Dawn Drain, Deep Ganguli, Dustin Li, Eli Tran-Johnson, Ethan Perez, Jamie Kerr, Jared Mueller, Jeffrey Ladish, Joshua Landau, Kamal Ndousse, Kamile Lukosuite, Liane Lovitt, Michael Sellitto, Nelson Elhage, Nicholas Schiefer, Noemi Mercado, Nova DasSarma, Robert Lasenby, Robin Larson, Sam Ringer, Scott Johnston, Shauna Kravec, Sheer El Showk, Stanislav Fort, Tamera Lanham, Timothy Telleen-Lawton, Tom Conerly, Tom Henighan, Tristan Hume, Samuel R. Bowman, Zac Hatfield-Dodds, Ben Mann, Dario Amodei, Nicholas Joseph, Sam McCandlish, Tom Brown, Jared Kaplan, 2022 arXiv preprint arXiv:2212.08073 DOI: 10.48550/arXiv.2212.08073 - This paper introduces Constitutional AI, a method for aligning models with a set of principles through AI feedback and critique, providing a basis for generating synthetic preference data via an LLM-as-a-Judge approach.