Fine-tuning LLMs on multi-source synthetic data mitigates distribution collapse and self-preference bias while increasing output quality relative to single-source or human-only fine-tuning.
The remaining 13,500 entries were used for training and will henceforth be called the training set
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Synthetic Eggs in Many Baskets: The Impact of Synthetic Data Diversity on LLM Fine-Tuning
Fine-tuning LLMs on multi-source synthetic data mitigates distribution collapse and self-preference bias while increasing output quality relative to single-source or human-only fine-tuning.