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Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large Language Models

Jie Chen, Yupeng Zhang, Bingning Wang, Wayne Xin Zhao, Ji-Rong Wen, Weipeng Chen

TL;DR

This work investigates imperfections in synthetic data for large language models, focusing on synthetic Q-A pairs that can cause pattern overfitting and shifts in output distributions, degrading instruction-following. It analyzes data-distribution differences and pattern simplification, then introduces an unlearning-based mitigation using a lower-bounded forgetting loss, replay, and bias-mitigation losses to recalibrate models while preserving benchmark performance. Empirical results show that the proposed unlearning strategy can reverse instruction-following issues at low training costs and even surpass larger baselines in chat-style evaluation, with partial losses in foundational capabilities kept in check. The approach offers a practical path to leveraging synthetic data more robustly for efficient LLM training, highlighting both the potential benefits and the need for careful mitigation of synthetic data flaws.

Abstract

Synthetic data has been proposed as a solution to address the issue of high-quality data scarcity in the training of large language models (LLMs). Studies have shown that synthetic data can effectively improve the performance of LLMs on downstream benchmarks. However, despite its potential benefits, our analysis suggests that there may be inherent flaws in synthetic data. The uniform format of synthetic data can lead to pattern overfitting and cause significant shifts in the output distribution, thereby reducing the model's instruction-following capabilities. Our work delves into these specific flaws associated with question-answer (Q-A) pairs, a prevalent type of synthetic data, and presents a method based on unlearning techniques to mitigate these flaws. The empirical results demonstrate the effectiveness of our approach, which can reverse the instruction-following issues caused by pattern overfitting without compromising performance on benchmarks at relatively low cost. Our work has yielded key insights into the effective use of synthetic data, aiming to promote more robust and efficient LLM training.

Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large Language Models

TL;DR

This work investigates imperfections in synthetic data for large language models, focusing on synthetic Q-A pairs that can cause pattern overfitting and shifts in output distributions, degrading instruction-following. It analyzes data-distribution differences and pattern simplification, then introduces an unlearning-based mitigation using a lower-bounded forgetting loss, replay, and bias-mitigation losses to recalibrate models while preserving benchmark performance. Empirical results show that the proposed unlearning strategy can reverse instruction-following issues at low training costs and even surpass larger baselines in chat-style evaluation, with partial losses in foundational capabilities kept in check. The approach offers a practical path to leveraging synthetic data more robustly for efficient LLM training, highlighting both the potential benefits and the need for careful mitigation of synthetic data flaws.

Abstract

Synthetic data has been proposed as a solution to address the issue of high-quality data scarcity in the training of large language models (LLMs). Studies have shown that synthetic data can effectively improve the performance of LLMs on downstream benchmarks. However, despite its potential benefits, our analysis suggests that there may be inherent flaws in synthetic data. The uniform format of synthetic data can lead to pattern overfitting and cause significant shifts in the output distribution, thereby reducing the model's instruction-following capabilities. Our work delves into these specific flaws associated with question-answer (Q-A) pairs, a prevalent type of synthetic data, and presents a method based on unlearning techniques to mitigate these flaws. The empirical results demonstrate the effectiveness of our approach, which can reverse the instruction-following issues caused by pattern overfitting without compromising performance on benchmarks at relatively low cost. Our work has yielded key insights into the effective use of synthetic data, aiming to promote more robust and efficient LLM training.
Paper Structure (16 sections, 5 equations, 4 figures, 7 tables)

This paper contains 16 sections, 5 equations, 4 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Experimental Setup
  4. Defect Analysis of Synthetic Data
  5. Data Distribution Differences
  6. Pattern Overfitting
  7. Unlearning-Based Mitigation Strategy
  8. Unlearning Strategy
  9. Unlearning Experiments
  10. Ablation Study
  11. Effectiveness of Unlearning Strategy
  12. Impact of Synthetic Data on Model Performance
  13. Efficacy of Bounded Forgetting Loss
  14. Conclusion
  15. Limitations
  16. ...and 1 more sections

Figures (4)

  • Figure 1: The overall pipeline of our study.
  • Figure 2: t-SNE visualization of data distributions. The clusters of NonSynth and SynthQA data show considerable non-overlap.
  • Figure 3: Kernel density estimation of token IDs for NonSynth and SynthQA data. The token frequency distribution for SynthQA data shows several small peaks, indicating high structural consistency for specific tokens compared to NonSynth data.
  • Figure 4: Kernel density estimation of perplexity values for OpenHermes-2.5 and MixedIns data using BaseLM, SynthLM and UnlearnLM. SynthLM shows a noticeable shift and reduced variance, while UnlearnLM corrects the distribution shift.