Domain-specific question answering (QA) systems for services face unique challenges in integrating heterogeneous knowledge sources while ensuring both accuracy and safety. Existing large language models often struggle with factual consistency and context alignment in sensitive domains such as healthcare policies and government welfare. In this work, we introduce Knowledge-Aware Reasoning and Memory-Augmented Adaptation (KARMA), a novel framework designed to enhance QA performance in care scenarios. KARMA incorporates a dual-encoder architecture to fuse structured and unstructured knowledge sources, a gated memory unit to dynamically regulate external knowledge integration, and a safety-aware controllable decoder that mitigates unsafe outputs using safety classification and guided generation techniques. Extensive experiments on a proprietary QA dataset demonstrate that KARMA outperforms strong baselines in both answer quality and safety. This study offers a comprehensive solution for building trustworthy and adaptive QA systems in service contexts.

翻译：面向服务的领域特定问答系统在整合异构知识源时面临独特挑战，需同时确保准确性与安全性。现有大型语言模型在医疗政策、政府福利等敏感领域常面临事实一致性与上下文对齐的困难。本研究提出知识感知推理与记忆增强自适应框架，旨在提升关怀场景下的问答性能。该框架采用双编码器架构融合结构化与非结构化知识源，通过门控记忆单元动态调控外部知识整合，并配备安全感知可控解码器，利用安全分类与引导生成技术抑制不安全输出。在专有问答数据集上的大量实验表明，该框架在答案质量与安全性方面均优于现有基线方法。本研究为构建服务场景中可信赖且自适应的问答系统提供了完整解决方案。