Home Blog Courses

All Courses

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

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

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 Real-World 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

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

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 Robust 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

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

Synthetic Data for LLM Pretraining and Fine-Tuning

Prerequisites: Basic Python, LLM familiarity

Level: Intermediate

What You'll Learn

Synthetic Data Generation Techniques
Understand and implement various methods for generating synthetic text data suitable for LLMs.
LLM Pretraining with Synthetic Data
Apply synthetic datasets to augment and improve the pretraining phase of Large Language Models.
LLM Fine-Tuning with Synthetic Data
Utilize synthetic data to effectively fine-tune LLMs for specific tasks and behaviors, including instruction following.
Synthetic Data Quality Evaluation
Develop skills to assess the quality, diversity, and utility of generated synthetic data.
Practical Implementation
Gain hands-on experience in building pipelines for synthetic data generation and its application in LLM workflows.

© 2025 ApX Machine Learning