图示真相：用于自动幻觉检测的结构化可视化
Graphing the Truth: Structured Visualizations for Automated Hallucination Detection in LLMs
AI总结：
本文提出通过结构化可视化技术，帮助自动检测大型语言模型中的幻觉问题，提升模型可靠性和响应质量。
摘要：
大型语言模型在理解和生成自然语言的能力上迅速发展。在企业环境中，它们经常通过闭源领域知识进行增强，以提供更具上下文信息的响应。然而，操作限制如有限的上下文窗口和预训练数据与提供的知识之间的不一致，常常导致幻觉，其中一些幻觉看起来非常可信，逃过了常规的人工审查。当前的缓解策略要么依赖于昂贵的大规模金牌标准问答整理，要么依赖于次级模型验证，这两种方法都无法提供确定性的保证。本文介绍了一个框架，将专有知识和模型生成内容组织成交互式视觉知识图谱。目标是通过将模型断言与底层真实来源链接并指示置信度水平，为终端用户提供清晰直观的幻觉区域视图。通过这种视觉界面，用户可以诊断不一致、识别薄弱推理链并提供纠正反馈。由此产生的有人参与的反馈流程可以增强模型的可靠性，并持续提高响应质量。
英文摘要：
Large Language Models have rapidly advanced in their ability to interpret and generate natural language. In enterprise settings, they are frequently augmented with closed-source domain knowledge to deliver more contextually informed responses. However, operational constraints such as limited context windows and inconsistencies between pre-training data and supplied knowledge often lead to hallucinations, some of which appear highly credible and escape routine human review. Current mitigation strategies either depend on costly, large-scale gold-standard Q\&A curation or rely on secondary model verification, neither of which offers deterministic assurance. This paper introduces a framework that organizes proprietary knowledge and model-generated content into interactive visual knowledge graphs. The objective is to provide end users with a clear, intuitive view of potential hallucination zones by linking model assertions to underlying sources of truth and indicating confidence levels. Through this visual interface, users can diagnose inconsistencies, identify weak reasoning chains, and supply corrective feedback. The resulting human-in-the-loop workflow creates a structured feedback loop that can enhance model reliability and continuously improve response quality.
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。
