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About this paper
Yiduo Guo, Zhen Guo, Chuanwei Huang, Zi-Ang Wang, Zekai Zhang, Haofei Yu, Huishuai Zhang, Yikang Shen
A framework for adapting large language models to specialized domains using only synthetically generated data is introduced, achieving performance comparable to or exceeding methods reliant on human-annotated data across various tasks.
Key insights
- High-quality synthetic data, when generated and curated appropriately, can match or surpass the performance of human-annotated data for LLM domain adaptation.
- A difficulty-adaptive curriculum learning approach effectively optimizes the training data distribution based on the base model's current performance, enhancing learning efficiency.
- The reinforcement learning component provides performance advantages over simple supervised fine-tuning on the same synthetic data.
Summary by alphaXiv.
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