EMMM，请解释我的模型！对话中可解释的机器生成文本检测
EMMM, Explain Me My Model! Explainable Machine Generated Text Detection in Dialogues
AI总结：
针对客户服务中机器生成文本检测的可解释性不足，提出EMMM先解释后检测框架，兼顾低延迟、高准确率与非专家可理解性，70%评估者偏好其输出。
摘要：
大型语言模型（LLM）在客户服务中的快速应用带来了新的风险，恶意行为者可以利用它们通过机器生成文本（MGT）进行大规模的用户冒充。当前的MGT检测方法在在线对话环境中常常表现不佳，降低了可信AI部署所必需的可信度和可解释性。在客户服务场景中，操作员通常是非专家用户，解释对于可信的MGT检测变得至关重要。在本文中，我们提出了EMMM，一个先解释后检测的框架，平衡了延迟、准确性和面向非专家的可解释性。实验结果表明，EMMM提供了非专家用户可理解的解释，70%的人类评估者更偏好其输出，同时与最先进的模型相比实现了有竞争力的准确性，并保持了低延迟，在1秒内生成输出。我们的代码和数据集已在https://github.com/AngieYYF/EMMM-explainable-chatbot-detection开源。
英文摘要：
The rapid adoption of large language models (LLMs) in customer service introduces new risks, as malicious actors can exploit them to conduct large-scale user impersonation through machine-generated text (MGT). Current MGT detection methods often struggle in online conversational settings, reducing the reliability and interpretability essential for trustworthy AI deployment. In customer service scenarios where operators are typically non-expert users, explanation become crucial for trustworthy MGT detection. In this paper, we propose EMMM, an explanation-then-detection framework that balances latency, accuracy, and non-expert-oriented interpretability. Experimental results demonstrate that EMMM provides explanations accessible to non-expert users, with 70\% of human evaluators preferring its outputs, while achieving competitive accuracy compared to state-of-the-art models and maintaining low latency, generating outputs within 1 second. Our code and dataset are open-sourced at https://github.com/AngieYYF/EMMM-explainable-chatbot-detection.
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。
