让普通用户看懂机器生成对话的检测原理，兼顾速度与准确
EMMM, Explain Me My Model! Explainable Machine Generated Text Detection in Dialogues
- 先解释再检测，用直观方式展示判断依据
- 非专家用户偏好度达70%，1秒内完成响应
- 适合客服场景，提升人机信任感
大型语言模型在客户服务中的广泛应用带来了新风险：恶意者可利用机器生成文本（MGT）进行大规模用户伪装。现有MGT检测方法在在线对话环境中表现不佳，影响了可信AI部署所需的可靠性与可解释性。针对客服场景中操作员多为非专家的情况，可解释性尤为重要。本文提出EMMM框架，通过先解释后检测的机制，在延迟、准确率和非专家可理解性之间取得平衡。实验表明，EMMM生成的解释对非专家友好，70%的人类评估者更倾向其输出；同时在准确率上媲美最先进模型，响应时间低于1秒。代码与数据集已开源。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。