Papers with distribution collapse
Synthetic Eggs in Many Baskets: The Impact of Synthetic Data Diversity on LLM Fine-Tuning (2026.findings-acl)
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| Challenge: | Increasing demand for training data is causing language models to be trained on synthetic data, a new study finds . fine-tuning models on synthetic datasets reduces self-preference bias . |
| Approach: | They investigate the impact of diversity of synthetic data on fine-tuned large language models. |
| Outcome: | The proposed model can mitigate distribution collapse, maintain diversity of output distribution, and reduce self-preference bias. |