Computer Science

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

J. Chen, Y. Zhang, et al.

This groundbreaking research by Jie Chen, Yupeng Zhang, Bingning Wang, Wayne Xin Zhao, Ji-Rong Wen, and Weipeng Chen explores the flaws in synthetic question-answer data used for training large language models. It highlights how these issues can lead to performance drops and proposes innovative unlearning techniques to mitigate these effects effectively and affordably.

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Abstract

Synthetic data offers a solution to the scarcity of high-quality data for training large language models (LLMs). While studies show its effectiveness in improving LLM performance, this paper analyzes inherent flaws in synthetic data, particularly question-answer (Q-A) pairs. The uniform format of synthetic data can lead to pattern overfitting and output distribution shifts, hindering instruction-following capabilities. The authors propose a mitigation method using unlearning techniques, demonstrating its effectiveness in reversing these negative effects at low cost.

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

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