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
Constructing Large-Scale Synthetic Corpora for Pretraining
New · Open Source
Kerb - LLM Development Toolkit
Python toolkit for building production-ready LLM applications. Modular utilities for prompts, RAG, agents, structured outputs, and multi-provider support.
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- Textbooks Are All You Need II: phi-1.5 technical report, Yuanzhi Li, Sébastien Bubeck, Ronen Eldan, Allie Del Giorno, Suriya Gunasekar, Yin Tat Lee, 2023 arXiv preprint arXiv:2309.05463 DOI: 10.48550/arXiv.2309.05463 - Introduces the concept of 'textbook-quality' data, a combination of synthetically generated text and carefully filtered web data, to pretrain small language models with strong reasoning and coding abilities.
- Unsupervised Data Augmentation for Consistency Training, Qizhe Xie, Zihang Dai, Eduard Hovy, Minh-Thang Luong, and Quoc V. Le, 2020 Advances in Neural Information Processing Systems - Describes data augmentation techniques like back-translation and TF-IDF based word replacement, showing their effectiveness in semi-supervised learning for NLP tasks, relevant for increasing data diversity.
- Deduplicating Training Data Makes Language Models Better, Katherine Lee, Daphne Ippolito, Andrew Nystrom, Chiyuan Zhang, Douglas Eck, Chris Callison-Burch, Nicholas Carlini, 2022 ACL 2022 DOI: 10.48550/arXiv.2107.06499 - Investigates the impact of data deduplication on large language model pretraining, demonstrating that removing near-duplicates leads to improved model performance and training efficiency.
- On the Dangers of Implicit Bias in LLM-Generated Text, Andrea Lampis, Eugenio Lomurno, Matteo Matteucci, 2023 arXiv preprint arXiv:2305.10118 DOI: 10.48550/arXiv.2305.10118 - Examines how implicit biases in LLMs can manifest in generated text and discusses the implications, highlighting the importance of bias mitigation strategies during synthetic corpus construction.
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