让大模型按需补知识，错哪补哪，不伤原有能力。
KSOD: Knowledge Supplement for LLMs On Demand
- 分析错误根源，定位缺失知识，针对性补充
- 在两个领域基准上提升性能，通用任务不变
- 适合需要精准知识增强的垂直场景使用
大型语言模型在多种任务中表现出色，但在特定领域任务中仍会出错。为提升其表现，我们提出KSOD（按需知识补充）框架，通过基于知识的监督微调（SFT）使模型自主补充缺失知识。该方法从知识不足的角度分析错误，识别可能导致错误的潜在缺失知识，随后在知识数据集上训练知识模块，并验证模型是否缺乏该知识。若确认缺失，则利用知识模块向模型补充相应内容。在特定知识上微调而非特定任务，实现任务与知识解耦。在两个领域基准和四个通用基准上的实验表明，KSOD能有效提升涉及补充知识的任务表现，同时保持其他任务性能不变。研究揭示了基于知识的SFT在增强大模型能力方面的潜力。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have demonstrated remarkable capabilities in various tasks, yet still produce errors in domain-specific tasks. To further improve their performance, we propose KSOD (Knowledge Supplement for LLMs On Demand), a novel framework that empowers LLMs to improve their capabilities with knowledge-based supervised fine-tuning (SFT). KSOD analyzes the causes of errors from the perspective of knowledge deficiency by identifying potential missing knowledge in LLM that may lead to the errors. Subsequently, KSOD tunes a knowledge module on knowledge dataset and verifies whether the LLM lacks the identified knowledge based on it. If the knowledge is verified, KSOD supplements the LLM with the identified knowledge using the knowledge module. Tuning LLMs on specific knowledge instead of specific task decouples task and knowledge and our experiments on two domain-specific benchmarks and four general benchmarks empirically demonstrate that KSOD enhances the performance of LLMs on tasks requiring the supplemented knowledge while preserving their performance on other tasks. Our findings shed light on the potential of improving the capabilities of LLMs with knowledge-based SFT.
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