Researchers Develop Method to Mine General Data for Improving Language Models in Specialized Domains
Researchers at arXiv have proposed NTK-Selector, a technique that identifies useful general-domain data to improve large language model performance in specialized fields with limited training data. The method uses a Neural Tangent Kernel approximation to select relevant general-domain samples that share similar reasoning patterns with domain-specific tasks. This approach achieved significant performance gains (8.7 and 5.1 points) on medical, financial, legal, and psychological domain tasks compared to traditional domain-only fine-tuning.
A new research paper introduces NTK-Selector, a data selection method designed to address the challenge of adapting large language models to low-resource specialized domains. Rather than relying exclusively on scarce domain-specific data, the approach leverages the vast amount of general-domain data that shares similar question-answer formats and reasoning patterns. The researchers developed a Jacobian-free Neural Tangent Kernel approximation to identify which general-domain samples are most beneficial for domain adaptation, making the technique computationally practical for pretrained LLMs. Extensive experiments across medical, financial, legal, and psychological domains demonstrated consistent improvements, with gains of +8.7 and +5.1 points on Llama3-8B-Instruct and Qwen3-8B models respectively, substantially outperforming both domain-only fine-tuning and existing data selection baselines.
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- arXiv cs.LGCenter
Rethinking Local Learning: A Cheaper and Faster Recipe for LLM Post-Training
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