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
Instruction Following Fine-Tuning using Generated Data
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- Self-Instruct: Aligning Language Models with Self-Generated Instructions, Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, Hannaneh Hajishirzi, 2022 ACL 2023 (Association for Computational Linguistics) DOI: 10.48550/arXiv.2212.10560 - A foundational paper introducing a method for large language models to generate their own instruction-following data, reducing the need for extensive human annotation.
- Finetuned Language Models Are Zero-Shot Learners, Jason Wei, Maarten Bosma, Vincent Y. Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V. Le, 2022 arXiv preprint arXiv:2109.01652 DOI: 10.48550/arXiv.2109.01652 - Introduces instruction tuning as a method to improve the zero-shot performance of language models across a wide range of tasks, laying groundwork for instruction-following models.