KARMA框架提升服务领域问答的准确与安全。
Memory-Augmented Knowledge Fusion with Safety-Aware Decoding for Domain-Adaptive Question Answering
- 双编码器融合结构化与非结构化知识
- 门控记忆单元动态调节外部知识
- 安全感知解码减少不安全输出
面向服务场景的领域特定问答系统在整合异构知识源时面临挑战，尤其在医疗政策、政府福利等敏感领域，现有大模型常出现事实不一致和上下文错位问题。本文提出KARMA框架，包含双编码器架构以融合结构化与非结构化知识，门控记忆单元动态调控外部知识引入，以及安全感知可控解码机制，通过安全分类与引导生成技术抑制不安全输出。在自建问答数据集上的大量实验表明，KARMA在答案质量与安全性方面均优于强基线模型。本研究为构建可信且可适应的服务类问答系统提供了完整解决方案。
原文摘要 · Abstract (English)
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.
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