Daily Arxiv

This is a page that curates AI-related papers published worldwide.
All content here is summarized using Google Gemini and operated on a non-profit basis.
Copyright for each paper belongs to the authors and their institutions; please make sure to credit the source when sharing.

Collaborative Evaluation of Deepfake Text with Deliberation-Enhancing Dialogue Systems

Created by
  • Haebom

Author

Jooyoung Lee, Xiaochen Zhu, Georgi Karadzhov, Tom Stafford, Andreas Vlachos, Dongwon Lee

Outline

This study proposes a collaborative effort using AI tools as a solution to the difficulty of distinguishing between human-generated and deepfake content due to the proliferation of generative models. Using DeepFakeDeLiBot, a consultation-enhanced chatbot for deepfake text detection, we demonstrate that group-based problem-solving significantly improves the accuracy of machine-generated paragraph identification compared to individual efforts. While using DeepFakeDeLiBot does not significantly improve overall performance, it improves group dynamics through increased participation, consensus building, and increased frequency and diversity of inference-based utterances. Furthermore, participants who highly valued the effectiveness of group collaboration also benefited from DeepFakeDeLiBot's performance. This highlights the potential of consultation chatbots to foster interactive and productive group dynamics while ensuring the accuracy of collaborative deepfake text detection.

Takeaways, Limitations

Takeaways:
We demonstrate that group-based problem solving is effective in improving deepfake text detection accuracy.
DeepFakeDeLiBot contributes to improving group dynamics (increased participation, consensus formation, and increased inference-based speech).
DeepFakeDeLiBot is more effective for participants with a high perception of the effectiveness of group collaboration.
Demonstrating the potential of collaborative deepfake text detection using a consultative chatbot.
Limitations:
Using DeepFakeDeLiBot had limited effect on improving overall performance.
The dataset and source code will be made public after the paper is accepted (currently inaccessible).
👍