通过自选内外知识，低成本提升特定领域问答能力
Select to Know: An Internal-External Knowledge Self-Selection Framework for Domain-Specific Question Answering
- 自选内外知识，分步内化领域知识
- 在医疗法律金融任务上超越现有方法
- 适合资源有限但需高精度领域的研究者
大型语言模型在通用问答中表现良好，但在特定领域常遇瓶颈。检索增强生成引入外部知识，却因噪声检索导致幻觉和延迟；持续预训练可内化领域知识，但成本高且跨领域适应性差。我们指出问题根源在于领域知识的长尾分布，使部分有用内部知识未被充分使用。我们认为知识获取应循序渐进，如同人类学习：先理解概念，再用于复杂推理。为此，我们提出S2K框架，通过内外知识自选策略与选择性监督微调，低成本内化领域知识，并设计结构化推理数据生成流程，结合GRPO提升推理能力。在医疗、法律、金融问答基准上的实验表明，S2K持续优于现有方法，性能接近领域预训练大模型，但成本显著更低。
原文摘要 · Abstract (English)
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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