Beyond Model Collapse: Scaling Up with Synthesized Data Requires Reinforcement

• URL: http://arxiv.org/abs/2406.07515v1
• Date: Tue, 11 Jun 2024 17:46:16 GMT
• Title: Beyond Model Collapse: Scaling Up with Synthesized Data Requires Reinforcement
• Authors: Yunzhen Feng, Elvis Dohmatob, Pu Yang, Francois Charton, Julia Kempe,
• Abstract summary: We investigate the use of feedback on synthesized data to prevent model collapse. We show that training from feedback-augmented synthesized data, either by pruning incorrect predictions or by selecting the best of several guesses, can prevent model collapse.
• Score: 11.6055501181235
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Synthesized data from generative models is increasingly considered as an alternative to human-annotated data for fine-tuning Large Language Models. This raises concerns about model collapse: a drop in performance of models fine-tuned on generated data. Considering that it is easier for both humans and machines to tell between good and bad examples than to generate high-quality samples, we investigate the use of feedback on synthesized data to prevent model collapse. We derive theoretical conditions under which a Gaussian mixture classification model can achieve asymptotically optimal performance when trained on feedback-augmented synthesized data, and provide supporting simulations for finite regimes. We illustrate our theoretical predictions on two practical problems: computing matrix eigenvalues with transformers and news summarization with large language models, which both undergo model collapse when trained on model-generated data. We show that training from feedback-augmented synthesized data, either by pruning incorrect predictions or by selecting the best of several guesses, can prevent model collapse, validating popular approaches like RLHF.

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