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Strength Lies in Differences! Improving Strategy Planning for Non-collaborative Dialogues via Diversified User Simulation (2403.06769v3)

Published 11 Mar 2024 in cs.CL

Abstract: We investigate non-collaborative dialogue agents, which are expected to engage in strategic conversations with diverse users, for securing a mutual agreement that leans favorably towards the system's objectives. This poses two main challenges for existing dialogue agents: 1) The inability to integrate user-specific characteristics into the strategic planning, and 2) The difficulty of training strategic planners that can be generalized to diverse users. To address these challenges, we propose Trip to enhance the capability in tailored strategic planning, incorporating a user-aware strategic planning module and a population-based training paradigm. Through experiments on benchmark non-collaborative dialogue tasks, we demonstrate the effectiveness of Trip in catering to diverse users.

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Citations (2)
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Summary

  • The paper demonstrates that integrating user-specific characteristics via Trip improves strategic planning and adaptability in non-collaborative dialogues.
  • It employs a user-aware module based on Theory-of-Mind principles to infer user mental states and adapt strategies dynamically.
  • Using population-based training with diverse user simulators, Trip achieves superior performance on benchmark non-collaborative tasks.

Enhancing Non-collaborative Dialogue Agents with Tailored Strategy Planning: A Study on the Trip Method

Introduction to the Study

The efficacy of dialogue agents in non-collaborative settings, such as negotiation and persuasion, hinges on their ability to strategically plan according to diverse user characteristics. However, current LLM-based agents fall short in this regard due to two main limitations: their general disregard for user-specific characteristics in strategic planning and a training paradigm that fails to foster adaptability to diverse users. To address these gaps, this paper introduces Trip, a method designed to bolster the tailored strategic planning capabilities of dialogue agents through a user-aware strategic planning module and a population-based training paradigm.

Key Challenges in Non-collaborative Dialogue

Non-collaborative dialogues present unique challenges, primarily the need for strategic planning tailored to individual users' characteristics. Current models typically struggle with this due to:

  1. Ignoring User-Specific Characteristics: Most existing agents lack mechanisms to integrate explicit user-specific characteristics, such as preferences and resistance levels, into their strategy formulations.
  2. Lack of Generalizability in Training: Conventional training paradigms, often reliant on single-user simulations, do not adequately prepare agents for the breadth of behavior found in diverse user populations. This contributes to a lack of flexibility and suboptimal performance when faced with previously unencountered user profiles.

Trip: A Novel Approach to Strategic Planning

To address these challenges, the paper presents Trip, which stands for Tailored stRategIc Planning. This method comprises two core components:

  • User-Aware Strategic Planning Module: Utilizes Theory-of-Mind (ToM) principles to infer user mental states and future actions during interactions. This information is then leveraged to adapt strategic plans accordingly.
  • Population-Based Training Paradigm: Instead of training with a singular user simulator, Trip employs a variety of simulators that represent different user personas and behaviors. This diversity in training environments is intended to enhance the agent's adaptability and performance across a wider spectrum of user interactions.

Methodological Insights and Contributions

The paper rigorously evaluates Trip’s effectiveness in improving tailored strategic planning in non-collaborative dialogues. Through experiments on benchmark tasks, Trip demonstrates superior performance in adapting to diverse users compared to baseline models. Specifically, it showcases:

  • Significant adaptability to diverse users, indicating that incorporating user-specific characteristics can profoundly impact the strategic planning of dialogue agents.
  • Improved performance across different non-collaborative tasks, suggesting that a more nuanced understanding of user characteristics and a broader training paradigm can effectively enhance agents' abilities to achieve favorable outcomes.

Furthermore, the paper provides a comprehensive analysis of the limitations inherent in current LLM-based dialogue agents, thus laying the groundwork for future advancements in this space.

Implications and Future Directions

The findings of this research underscore the importance of user-specific strategic planning in enhancing the capabilities of non-collaborative dialogue agents. The introduction of Trip marks a significant step towards creating more adaptable and effective agents capable of navigating the complexities of human-like negotiation and persuasion.

Looking ahead, this work paves the way for further exploration into:

  • The Integration of Advanced User Characteristic Modeling: Future research could explore the nuances of user behavior and preference modeling, potentially incorporating real-time feedback and adjustment mechanisms.
  • Scalability of Population-Based Training Paradigms: Investigating efficient and cost-effective ways to scale population-based training could be beneficial, especially considering the resource-intensive nature of training large models.

In summary, the paper presents a compelling case for the necessity of tailored strategic planning in non-collaborative dialogue agents, offering a robust solution through the Trip method. As the field of conversational AI continues to evolve, the insights gained here will undoubtedly contribute to the development of more nuanced, flexible, and effective dialogue systems.

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