Key Points
1. The paper explores the potential of Large Language Models (LLMs) to shape user perspectives and influence their decisions across domains like investment, credit cards, insurance, retail, and behavioral change support systems.
2. The paper presents a sophisticated multi-agent framework where a consortium of agents (conversation agent, advisor agent, moderator, and retrieval agent) operate collaboratively to enhance the persuasive efficacy of the LLM.
3. The paper employs simulated personas with diverse personality types in insurance, banking, and retail domains to evaluate the proficiency of LLMs in recognizing, adjusting to, and influencing various personality types.
4. The paper examines the resistance mechanisms employed by LLM simulated personas and quantifies persuasion through measurable surveys, LLM-generated scores on conversations, and user decisions (purchase or non-purchase).
5. The paper found that LLM-based agents can significantly enhance the persuasive efficacy compared to standalone LLM approaches by continuously analyzing the user's mood, resistance, and inclination, and employing a combination of rule-based and LLM-based resistance-persuasion mapping techniques.
6. The paper observed that the introduction of emotion modifiers in user agents diminished the positive shift in user perspectives compared to the baseline scenario, indicating a behavioral change in the user agents.
7. The paper found that despite a negative purchase decision, the user's perspective can change positively in the baseline scenario, but when emotion modifiers are used, "no buy" decisions induced a negative change in user perspectives.
8. The paper observed that user agents tend to end conversations quickly when the information provided by the sales agent seems inadequate, indicating the need for strengthening the domain context of the chatbots.
9. The paper proposes future enhancements to the sales agents with memory and tools for users to look up data during the conversation, making the conversation more dynamic and informed.
Summary
Research Objective and Framework
This research paper explores how large language models (LLMs) can influence user perspectives and decision-making in tasks such as selecting insurance policies, investment plans, and credit options. The authors present a multi-agent framework where a consortium of agents collaborate to engage in persuasive dialogue with user agents. The primary agent directly interacts with the user agent through persuasive dialogue, while auxiliary agents perform tasks like information retrieval, response analysis, persuasion strategy development, and fact validation. Empirical evidence demonstrates that this collaborative approach significantly enhances the persuasive efficacy of the LLM.
Evaluation of LLM's User Influence
The researchers employ simulated personas with various personality types to evaluate the LLM's ability to recognize, adjust to, and influence different user profiles in the insurance, banking, and retail domains. They also examine the resistance mechanisms employed by the LLM-simulated personas.
Measurement of Persuasive Efficacy
Persuasion is quantified through measurable surveys before and after interactions, LLM-generated scores on the conversation, and user decisions (purchase or non-purchase). The findings show that the LLM-based agents can effectively persuade users, with a 35% positive decision rate in the baseline setting and 28% with emotion modifiers enabled.
User Termination of Conversations
However, the authors also observe that the user agents tend to terminate conversations quickly when the information provided by the sales agent is perceived as inadequate. This indicates the need for strengthening the domain context and knowledge of the chatbots.
Implications and Recommendations
The paper highlights the potential of LLMs to shape user perspectives and influence decision-making, while also emphasizing the importance of developing robust persuasion strategies and enhancing the conversational abilities of these models to provide users with comprehensive and convincing information.
Reference: https://arxiv.org/abs/2408.15879