通过显式知识边界建模增强大语言模型可靠性
Enhancing LLM Reliability via Explicit Knowledge Boundary Modeling
AI总结：
本文提出显式知识边界建模（EKBM）框架，通过快速与慢速推理结合及混合训练，在对话状态跟踪任务中提升大语言模型可靠性，平衡准确性与计算效率。
摘要：
大语言模型（LLMs）容易产生幻觉，这源于其自我认知的错位，尤其是在处理超出其知识边界的查询时。现有的缓解策略采用不确定性估计或查询拒绝机制，但存在计算效率低和牺牲有用性的问题。为解决这些问题，我们提出了显式知识边界建模（EKBM）框架，该框架整合了快速与慢速推理系统，以协调可靠性与可用性。该框架首先使用快速思考模型生成带有置信度标签的响应，从而能够立即利用高置信度的输出，而不确定的预测则触发慢速细化模型以提高准确性。为使模型行为与我们的目标一致，我们提出了一种混合训练流程，在不降低任务性能的情况下增强自我认知。在对话状态跟踪任务上的评估表明，EKBM相比基于不确定性的基线方法实现了更优的模型可靠性。进一步分析显示，细化过程在保持低计算开销的同时显著提升了准确性。该框架为在错误敏感应用中部署可靠的LLM建立了一种可扩展的范式，有效平衡了准确性与实际效用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
