Mining Useful General Data for Low-Resource Domain Adaptation
Researchers have developed a new method called NTK-Selector to improve the adaptation of large language models to low-resource domains. This technique mines useful general-domain data, specifically chain-of-thought examples, to supplement limited domain-specific information. By approximating the Neural Tangent Kernel, NTK-Selector identifies beneficial general-domain samples, leading to significant performance gains across various specialized fields. AI
IMPACT Enhances LLM utility in specialized fields by leveraging general data, potentially reducing the need for extensive domain-specific datasets.