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
Methods for Data Masking and Perturbation
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- Anonymizing Health Data, Khaled El Emam, and Lydia Aysegul Neşeoğlu, 2018 (Springer) DOI: 10.1007/978-3-319-94101-2 - This book provides comprehensive coverage of de-identification methods, including masking, generalization, and redaction, for protecting sensitive information in text and other data types.
- Data Augmentation in NLP: A Survey, Sanjana Gupta, R. M. K. R. Namburi, Sai Nikhil, M. Ramakrishna, and P. S. R. N. Praveen, 2023 EAI Endorsed Transactions on AI and Robotics, Vol. 5 (European Alliance for Innovation) DOI: 10.4108/eetair.v5i2.27448 - A recent survey offering a broad overview of data augmentation techniques specifically for Natural Language Processing, including those relevant to generating synthetic text variants.
- Guide to Protecting the Confidentiality of Personally Identifiable Information (PII), Marianne Swanson, Amy Bell, Marshall Abrams, and David Phillips, 2010 NIST Special Publication 800-122 (National Institute of Standards and Technology (NIST)) - This NIST publication provides essential guidance on safeguarding PII, including principles and techniques for de-identification and masking to reduce privacy risks.