通过显式建模知识边界，提升大模型可靠性与实用性。
Enhancing LLM Reliability via Explicit Knowledge Boundary Modeling
- 双系统设计：快速模型生成置信响应，低信度时触发慢速修正。
- 在对话状态追踪任务中，准确率显著优于基于不确定性的基线。
- 兼顾高可靠性和低计算开销，适合对错误敏感的实用场景。
大语言模型因自我认知错位，在处理超出其知识范围的查询时易产生幻觉。现有缓解策略如不确定性估计或查询拒绝机制，存在计算效率低且牺牲帮助性的问题。为此，我们提出显式知识边界建模（EKBM）框架，融合快速与慢速推理系统，平衡可靠性与可用性。快速模型生成带置信度标签的响应，高置信输出可即时使用；低置信预测则触发慢速优化模型以提升准确性。为对齐模型行为，设计混合训练流程，在不损害任务性能的前提下增强自知能力。对话状态追踪任务评估显示，EKBM在可靠性上优于基于不确定性的基线。进一步分析表明，优化显著提升准确率，同时保持低计算开销。该框架为高可靠性大模型在误差敏感应用中的部署提供了可扩展范式。
原文摘要 · Abstract (English)
Large language models (LLMs) are prone to hallucination stemming from misaligned self-awareness, particularly when processing queries exceeding their knowledge boundaries. While existing mitigation strategies employ uncertainty estimation or query rejection mechanisms, they suffer from computational efficiency and sacrificed helpfulness. To address these issues, we propose the Explicit Knowledge Boundary Modeling (EKBM) framework, integrating fast and slow reasoning systems to harmonize reliability and usability. The framework first employs a fast-thinking model to generate confidence-labeled responses, enabling immediate utilization of high-confidence outputs, whereas uncertain predictions trigger a slow refinement model for accuracy improvement. To align model behavior with our proposed object, we propose a hybrid training pipeline, enhancing self-awareness without degrading task performance. Evaluations on dialogue state tracking tasks demonstrate that EKBM achieves superior model reliability over uncertainty-based baselines. Further analysis reveals that refinement substantially boosts accuracy while maintaining low computational overhead. The framework establishes a scalable paradigm for deploying reliable LLMs in error-sensitive applications, effectively balancing accuracy and practical utility.
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