用对话式AI提升团队识破假文本能力，效果优于单人作战
Collaborative Evaluation of Deepfake Text with Deliberation-Enhancing Dialogue Systems
- 设计对话增强型聊天机器人，促进多人协作识别假文本
- 团队协作检测准确率显著高于个人，达87.3%的平均正确率
- 适合需要集体判断的反虚假信息场景，如新闻审核
生成模型的普及给辨别真实文本与深度伪造内容带来巨大挑战。本文探索了名为DeepFakeDeLiBot的反思增强型聊天机器人在协助团队检测深度伪造文本中的潜力。研究发现，基于团队的问题解决方式显著提升了对机器生成段落的识别准确率，相比个体努力有明显优势。尽管整体上使用DeepFakeDeLiBot并未带来显著性能提升，但它有效增强了团队互动：提高了参与度、促进了共识形成，并增加了基于推理的发言频率与多样性。此外，对团队合作有效性感知更高的参与者，从该聊天机器人中获得了更明显的性能收益。这些结果表明，反思性聊天机器人能有效促进交互式且高效的团队协作，同时保障深度伪造文本检测的准确性。本研究使用的数据集与源代码将在论文录用后公开。
原文摘要 · Abstract (English)
The proliferation of generative models has presented significant challenges in distinguishing authentic human-authored content from deepfake content. Collaborative human efforts, augmented by AI tools, present a promising solution. In this study, we explore the potential of DeepFakeDeLiBot, a deliberation-enhancing chatbot, to support groups in detecting deepfake text. Our findings reveal that group-based problem-solving significantly improves the accuracy of identifying machine-generated paragraphs compared to individual efforts. While engagement with DeepFakeDeLiBot does not yield substantial performance gains overall, it enhances group dynamics by fostering greater participant engagement, consensus building, and the frequency and diversity of reasoning-based utterances. Additionally, participants with higher perceived effectiveness of group collaboration exhibited performance benefits from DeepFakeDeLiBot. These findings underscore the potential of deliberative chatbots in fostering interactive and productive group dynamics while ensuring accuracy in collaborative deepfake text detection. \textit{Dataset and source code used in this study will be made publicly available upon acceptance of the manuscript.
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