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
Building Pipelines for Data Filtering and Cleansing
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- Deduplicating Training Data Makes Language Models Better, Katherine Lee, Daphne Ippolito, Andrew Nystrom, Chiyuan Zhang, Douglas Eck, Chris Callison-Burch, Nicholas Carlini, 2021 Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics DOI: 10.48550/arXiv.2107.06499 - Demonstrates that deduplication of training data significantly improves the performance of language models, supporting the need for near-duplicate filtering.
- Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks, Nils Reimers, Iryna Gurevych, 2019 Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing DOI: 10.48550/arXiv.1908.10084 - Introduces Sentence-BERT, a method for generating semantically meaningful sentence embeddings, which is fundamental for embedding-based near-duplicate detection.
- Hugging Face Datasets Library Documentation, Hugging Face, 2023 (Hugging Face) - Provides comprehensive documentation for the Hugging Face
datasetslibrary, showing how to efficiently load, process, and filter large text datasets for machine learning applications.