[2503.04945] Collaborative Evaluation of Deepfake Text with Deliberation-Enhancing Dialogue Systems

Computer Science > Computation and Language arXiv:2503.04945 (cs) [Submitted on 6 Mar 2025 (v1), last revised 24 Mar 2026 (this version, v3)] Title:Collaborative Evaluation of Deepfake Text with Deliberation-Enhancing Dialogue Systems Authors:Jooyoung Lee, Xiaochen Zhu, Georgi Karadzhov, Tom Stafford, Andreas Vlachos, Dongwon Lee View a PDF of the paper titled Collaborative Evaluation of Deepfake Text with Deliberation-Enhancing Dialogue Systems, by Jooyoung Lee and 5 other authors View PDF HTML (experimental) Abstract: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 delibe...