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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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References (45)
  1. How well can llms negotiate? negotiationarena platform and analysis. arXiv preprint arXiv:2402.05863.
  2. Social influence dialogue systems: A survey of datasets and models for social influence tasks. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 750–766.
  3. Be selfish, but wisely: Investigating the impact of agent personality in mixed-motive human-agent interactions. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 13078–13092, Singapore. Association for Computational Linguistics.
  4. Controllable mixed-initiative dialogue generation through prompting. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 951–966, Toronto, Canada. Association for Computational Linguistics.
  5. A survey on proactive dialogue systems: Problems, methods, and prospects. arXiv preprint arXiv:2305.02750.
  6. Prompting and evaluating large language models for proactive dialogues: Clarification, target-guided, and non-collaboration.
  7. Plug-and-play policy planner for large language model powered dialogue agents. arXiv preprint arXiv:2311.00262.
  8. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
  9. Towards measuring the representation of subjective global opinions in language models. arXiv preprint arXiv:2306.16388.
  10. Resper: Computationally modelling resisting strategies in persuasive conversations. arXiv preprint arXiv:2101.10545.
  11. Strategies and motives for resistance to persuasion: An integrative framework. Frontiers in psychology, 6:1201.
  12. Ivar Frisch and Mario Giulianelli. 2024. Llm agents in interaction: Measuring personality consistency and linguistic alignment in interacting populations of large language models. arXiv preprint arXiv:2402.02896.
  13. Improving language model negotiation with self-play and in-context learning from ai feedback.
  14. Lewis R Goldberg. 1992. The development of markers for the big-five factor structure. Psychological assessment, 4(1):26.
  15. Decoupling strategy and generation in negotiation dialogues. arXiv preprint arXiv:1808.09637.
  16. Enhancing large language model induced task-oriented dialogue systems through look-forward motivated goals.
  17. Bayes-adaptive monte-carlo planning and learning for goal-oriented dialogues. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 7994–8001.
  18. Evaluating and inducing personality in pre-trained language models. Advances in Neural Information Processing Systems, 36.
  19. Personallm: Investigating the ability of gpt-3.5 to express personality traits and gender differences. arXiv preprint arXiv:2305.02547.
  20. Evaluating persuasion strategies and deep reinforcement learning methods for negotiation dialogue agents. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 480–484, Valencia, Spain. Association for Computational Linguistics.
  21. Are llms effective negotiators? systematic evaluation of the multifaceted capabilities of llms in negotiation dialogues. arXiv preprint arXiv:2402.13550.
  22. Interacting with non-cooperative user: A new paradigm for proactive dialogue policy.
  23. Deal or no deal? end-to-end learning of negotiation dialogues. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2443–2453.
  24. Legoeval: An open-source toolkit for dialogue system evaluation via crowdsourcing. arXiv preprint arXiv:2105.01992.
  25. One cannot stand for everyone! leveraging multiple user simulators to train task-oriented dialogue systems. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1–21.
  26. Shima Rahimi Moghaddam and Christopher J Honey. 2023. Boosting theory-of-mind performance in large language models via prompting. arXiv preprint arXiv:2304.11490.
  27. David Premack and Guy Woodruff. 1978. Does the chimpanzee have a theory of mind? Behavioral and brain sciences, 1(4):515–526.
  28. Personality traits in large language models. arXiv preprint arXiv:2307.00184.
  29. Neural theory-of-mind? on the limits of social intelligence in large lms. arXiv preprint arXiv:2210.13312.
  30. Susanne G Scott and Reginald A Bruce. 1995. Decision-making style: The development and assessment of a new measure. Educational and psychological measurement, 55(5):818–831.
  31. Role play with large language models. Nature, 623(7987):493–498.
  32. How to build user simulators to train rl-based dialog systems. arXiv preprint arXiv:1909.01388.
  33. Does role-playing chatbots capture the character personalities? assessing personality traits for role-playing chatbots. arXiv preprint arXiv:2310.17976.
  34. Persuasion for good: Towards a personalized persuasive dialogue system for social good. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5635–5649, Florence, Italy. Association for Computational Linguistics.
  35. Ronald J Williams. 1992. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine learning, 8:229–256.
  36. Heinz Wimmer and Josef Perner. 1983. Beliefs about beliefs: Representation and constraining function of wrong beliefs in young children’s understanding of deception. Cognition, 13(1):103–128.
  37. Improving dialog systems for negotiation with personality modeling.
  38. Prompt-based Monte-Carlo tree search for goal-oriented dialogue policy planning. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 7101–7125, Singapore. Association for Computational Linguistics.
  39. Let’s negotiate! a survey of negotiation dialogue systems. arXiv preprint arXiv:2402.01097.
  40. Ask an expert: Leveraging language models to improve strategic reasoning in goal-oriented dialogue models. arXiv preprint arXiv:2305.17878.
  41. Towards effective automatic debt collection with persona awareness. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 32–45, Singapore. Association for Computational Linguistics.
  42. Explaining agent behavior with large language models. arXiv preprint arXiv:2309.10346.
  43. How far are large language models from agents with theory-of-mind? arXiv preprint arXiv:2310.03051.
  44. Sotopia: Interactive evaluation for social intelligence in language agents. arXiv preprint arXiv:2310.11667.
  45. Augmenting non-collaborative dialog systems with explicit semantic and strategic dialog history.
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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