Overview
This research introduces Dialogue Policy Selection (DiPS), a Q-learning framework for dynamic strategy selection in high-stakes persuasive dialogue. The framework addresses challenges associated with Large Language Models (LLMs) in persuasion, particularly their struggles with tailoring strategies to individual personalities and concerns rather than employing a one-size-fits-all approach. DiPS was developed and evaluated within the specific high-stakes domain of a fire-rescue scenario, where an operator's objective is to persuade a resident to evacuate.
Research Context
The application of LLMs in persuasive dialogue, especially in high-stakes scenarios, presents difficulties due to the inherent variability in human personalities and concerns. Effective persuasion often necessitates individualized strategies that adapt to the evolving conversational context. The research identifies the need for a system capable of dynamically selecting appropriate persuasion policies to enhance success rates in critical situations. The fire-rescue scenario serves as a representative high-stakes domain for investigating these challenges, where successful persuasion (evacuation) is paramount.
Approach
The core of the proposed approach is Dialogue Policy Selection (DiPS, a Q-learning framework. This framework is designed to dynamically select persuasion strategies. A key component of DiPS is a critic, which is trained with the objective of maximizing the probability of evacuation success. This critic operates by selecting a persuasion policy at each turn of the conversation. The selection process is informed by the resident's recent utterances, indicating an adaptive mechanism to conversational context. The development involved training this critic to tailor its policy choices based on real-time dialogue input.
Following its development, DiPS was subjected to evaluation against several baseline methods. These baselines included a zero-shot LLM approach and a generic Retrieval-Augmented Generation (RAG)-augmented approach. The evaluation encompassed two distinct interaction environments: simulated interactions and real human interactions. This dual evaluation strategy aimed to assess the framework's performance across different levels of realistic engagement.
Findings
The evaluation of DiPS yielded several findings regarding its effectiveness in the high-stakes fire-rescue scenario:
- DiPS achieved higher evacuation success rates when compared to a zero-shot LLM. This indicates an improvement over basic LLM application without specific policy selection mechanisms.
- DiPS also demonstrated superior evacuation success in comparison to a generic RAG-augmented approach. This suggests that the dynamic policy selection mechanism within DiPS offers advantages beyond simple information retrieval augmentation.
- These results were observed consistently across both simulated interactions and interactions involving real human participants. This dual validation provides support for the framework's performance in varied testing environments.
Why This Matters
The findings regarding DiPS suggest a method for enhancing the effectiveness of conversational agents in critical persuasive scenarios. By dynamically adapting persuasion strategies based on conversational context, the framework addresses a limitation of current LLMs, which often apply uniform approaches. The observed increase in evacuation success in fire-rescue contexts indicates a potential for improved outcomes in situations requiring urgent action and tailored communication.