Synthetic Eggs in Many Baskets: The Impact of Synthetic Data Diversity on LLM Fine-Tuning

2025-11-03

AI Summary

This study investigates how the diversity of synthetic data sources affects fine-tuning behavior of large language models (LLMs), focusing on three critical challenges: distributional collapse, adversarial robustness, and self-preference bias. We propose a multi-source synthetic data generation and comparative evaluation framework integrating distributional diversity metrics, adversarial sample testing, and quantitative preference bias analysis. Experiments demonstrate that, compared to single-source synthetic data, multi-source approaches significantly mitigate distributional collapse—enhancing output diversity and quality stability; improve adversarial robustness, rendering potential risks more controllable; and substantially reduce self-preference bias, achieving performance comparable to human-annotated data. To our knowledge, this is the first systematic investigation revealing the pivotal role of multi-source synthetic data in balancing safety and performance during LLM fine-tuning. Our work establishes a reproducible, high-quality paradigm for training LLMs with low bias, strong robustness, and reliable generalization.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Large Multimodal Models (LMMs)
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

As synthetic data becomes widely used in language model development, understanding its impact on model behavior is crucial. This paper investigates the impact of the diversity of sources of synthetic data on fine-tuned large language models. We focus on three key dimensions: distribution collapse, adversarial robustness, and self-preference bias. Our findings reveal that fine-tuning models on synthetic data from diverse sources can mitigate distribution collapse, preserving the breadth of the output distribution and the diversity of the output text. Furthermore, while both human and synthetic fine-tuning data can remove safeguards, the latter preserves higher output quality, thus making outputs potentially more usable and dangerous. Finally, fine-tuning reduces self-preference bias, with human data being the most effective, followed by multi-source synthetic data.

Problem

• Investigating synthetic data source diversity impact on fine-tuned LLMs
• Mitigating distribution collapse through diverse synthetic fine-tuning data
• Analyzing self-preference bias reduction across different fine-tuning data types

Innovation

• Diverse synthetic data sources mitigate distribution collapse
• Synthetic data preserves output quality while removing safeguards
• Multi-source synthetic fine-tuning reduces self-preference bias