EMNLP 2023|What are the pitfalls of using LLM synthetic data to train models?

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EMNLP 2023|What are the pitfalls of using LLM synthetic data to train models?

EMNLP 2023|What are the pitfalls of using LLM synthetic data to train models?

Content introduction

This content explores some of the pitfalls when using large language models (LLMs) to generate synthetic data to train text classification models. The study found that while LLMs can generate data, the quality and validity of these synthetic data depend heavily on the subjectivity of the task. A key finding is that models trained using synthetic data generated by LLMs perform poorly compared to models trained using real data, especially in tasks with high subjectivity. The study also highlights that LLMs for few-shot generation will produce more diverse data samples than zero-shot generation, which is beneficial for model training. This insight is particularly important for researchers and practitioners in the fields of machine learning and natural language processing, as it highlights the importance of considering task subjectivity and data diversity when leveraging data generated by LLMs for training.

Automatic summary

– The potential and limitations of large language models (LLMs) using synthetic data in text classification tasks are discussed.
– The effectiveness of synthetic data is affected by the subjectivity of the task. The higher the subjectivity, the worse the effect of synthetic data.
– The effect of synthetic data generated using the few-shot method is generally better than that generated using the zero-shot method.
– Real-world data is more diverse than synthetic data, and a higher diversity dataset helps improve the model’s generalization ability.
– Highly subjective task instances may limit model performance, even in a few-shot setting.
– Synthetic data may not be sufficient to support the training of high-performance models, especially in tasks with high subjectivity.

Original link: https://mp.weixin.qq.com/s/FqtL2ZQ2Xdb3AD3ENpP7zw

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