Select to Know: An Internal-External Knowledge Self-Selection Framework for Domain-Specific Question Answering

• URL: http://arxiv.org/abs/2508.15213v2
• Date: Thu, 18 Sep 2025 11:35:01 GMT
• Title: Select to Know: An Internal-External Knowledge Self-Selection Framework for Domain-Specific Question Answering
• Authors: Bolei He, Xinran He, Run Shao, Shanfu Shu, Xianwei Xue, Mingquan Cheng, Haifeng Li, Zhenhua Ling,
• Abstract summary: Large Language Models (LLMs) perform well in general QA but often struggle in domain-specific scenarios.<n>We propose Selct2Know (S2K), a cost-effective framework that internalizes domain knowledge through an internal-external knowledge self-selection strategy.<n> Experiments on medical, legal, and financial QA benchmarks show that S2K consistently outperforms existing methods.
• Score: 33.45313626747207
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) perform well in general QA but often struggle in domain-specific scenarios. Retrieval-Augmented Generation (RAG) introduces external knowledge but suffers from hallucinations and latency due to noisy retrievals. Continued pretraining internalizes domain knowledge but is costly and lacks cross-domain flexibility. We attribute this challenge to the long-tail distribution of domain knowledge, which leaves partial yet useful internal knowledge underutilized. We further argue that knowledge acquisition should be progressive, mirroring human learning: first understanding concepts, then applying them to complex reasoning. To address this, we propose Selct2Know (S2K), a cost-effective framework that internalizes domain knowledge through an internal-external knowledge self-selection strategy and selective supervised fine-tuning. We also introduce a structured reasoning data generation pipeline and integrate GRPO to enhance reasoning ability. Experiments on medical, legal, and financial QA benchmarks show that S2K consistently outperforms existing methods and matches domain-pretrained LLMs with significantly lower cost.

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