Enhancing Persuasive Dialogue Agents by Synthesizing Cross-Disciplinary Communication Strategies
arXiv:2602.22696v1 Announce Type: new Abstract: Current approaches to developing persuasive dialogue agents often rely on a limited set of predefined persuasive strategies that fail to capture the complexity of real-world interactions. We applied a cross-disciplinary approach to develop a framework for designing persuasive dialogue agents that draws on proven strategies from social psychology, behavioral economics, and communication theory. We validated our proposed framework through experiments on two distinct datasets: the Persuasion for Good dataset, which represents a specific in-domain scenario, and the DailyPersuasion dataset, which encompasses a wide range of scenarios. The proposed framework achieved strong results for both datasets and demonstrated notable improvement in the persuasion success rate as well as promising generalizability. Notably, the proposed framework also excelled at persuading individuals with initially low intent, which addresses a critical challenge for p
arXiv:2602.22696v1 Announce Type: new Abstract: Current approaches to developing persuasive dialogue agents often rely on a limited set of predefined persuasive strategies that fail to capture the complexity of real-world interactions. We applied a cross-disciplinary approach to develop a framework for designing persuasive dialogue agents that draws on proven strategies from social psychology, behavioral economics, and communication theory. We validated our proposed framework through experiments on two distinct datasets: the Persuasion for Good dataset, which represents a specific in-domain scenario, and the DailyPersuasion dataset, which encompasses a wide range of scenarios. The proposed framework achieved strong results for both datasets and demonstrated notable improvement in the persuasion success rate as well as promising generalizability. Notably, the proposed framework also excelled at persuading individuals with initially low intent, which addresses a critical challenge for persuasive dialogue agents.
Executive Summary
This article presents a novel framework for designing persuasive dialogue agents by synthesizing cross-disciplinary communication strategies from social psychology, behavioral economics, and communication theory. The proposed framework was validated through experiments on two distinct datasets, achieving strong results and notable improvement in persuasion success rate and generalizability. Notably, the framework excelled at persuading individuals with initially low intent, addressing a critical challenge in persuasive dialogue agents. This research has significant implications for the development of more effective and adaptable persuasive dialogue agents, with potential applications in areas such as marketing, education, and public health. The study's findings and framework contribute meaningfully to the growing body of research in human-computer interaction and artificial intelligence.
Key Points
- ▸ The proposed framework integrates strategies from social psychology, behavioral economics, and communication theory to enhance persuasive dialogue agents.
- ▸ The framework achieved strong results and notable improvement in persuasion success rate and generalizability across two distinct datasets.
- ▸ The framework excelled at persuading individuals with initially low intent, addressing a critical challenge in persuasive dialogue agents.
Merits
Comprehensive Approach
The framework's cross-disciplinary approach provides a more comprehensive and robust understanding of persuasive dialogue agents.
Improved Persuasion Success Rate
The framework achieved notable improvement in persuasion success rate, demonstrating its effectiveness in real-world interactions.
Demerits
Limited Generalizability
While the framework demonstrated promising generalizability, further research is needed to validate its effectiveness across diverse scenarios and populations.
Dependence on Data Quality
The framework's performance relies heavily on the quality of the training data, which may be a limitation in real-world applications where data quality is variable.
Expert Commentary
The article presents a significant contribution to the field of human-computer interaction and artificial intelligence. The proposed framework's cross-disciplinary approach and improved persuasion success rate demonstrate its potential for real-world applications. However, further research is needed to address the limitations of the framework, particularly its dependence on data quality and limited generalizability. The study's findings also raise important questions about the ethical implications of persuasive dialogue agents, which policymakers and developers must consider in their applications.
Recommendations
- ✓ Future research should focus on further validating the framework's effectiveness across diverse scenarios and populations.
- ✓ Developers should prioritize the collection and use of high-quality training data to ensure the framework's performance in real-world applications.
