METRO: Towards Strategy Induction from Expert Dialogue Transcripts for Non-collaborative Dialogues
Summary: arXiv:2604.11427v2 Announce Type: replace-cross
Abstract
Developing non-collaborative dialogue agents traditionally requires the manual, unscalable codification of expert strategies. We propose METRO, a method that leverages large language models to autonomously induce both strategy actions and planning logic directly from raw transcripts. METRO formalizes expert knowledge into a Strategy Forest, a hierarchical structure that captures both short-term responses (nodes) and long-term strategic foresight (branches).
Key Findings
Experimental results across two benchmarks show that METRO demonstrates promising performance, outperforming existing methods by an average of 9%-10%. Our further analysis not only reveals the success behind METRO—strategic behavioral diversity and foresight—but also demonstrates its robust cross-task transferability. This offers new insights into building non-collaborative agents in a cost-effective and scalable way.
Introduction
The field of artificial intelligence has seen significant advancements in the development of dialogue systems. Traditional methods for creating non-collaborative dialogue agents often rely on labor-intensive processes that are not easily scalable. The METRO framework seeks to address these challenges by utilizing the capabilities of large language models to extract strategic knowledge from expert dialogue transcripts.
Methodology
METRO operates by analyzing raw transcripts of expert dialogues and organizing the extracted strategies into a hierarchical model called a Strategy Forest. This model consists of:
- Nodes: Representing short-term responses that an agent can take during a dialogue.
- Branches: Indicating long-term strategic plans that guide the overall dialogue flow.
This approach not only streamlines the induction of strategies but also enhances the agent’s ability to foresee the consequences of its actions in a dialogue.
Experimental Results
In various benchmarks, METRO consistently outperformed existing methods, showcasing an average performance improvement of 9%-10%. The results indicate that agents trained under the METRO framework exhibit a greater diversity in strategic behaviors, enabling them to adapt to different dialogue scenarios more effectively.
Analysis and Insights
Further analysis into METRO has revealed two critical factors contributing to its success:
- Strategic Behavioral Diversity: Agents demonstrate a wide array of responses, allowing them to engage in more natural and varied interactions.
- Strategic Foresight: The ability to predict the outcomes of actions enhances the decision-making process during dialogues.
Additionally, METRO has shown robust transferability across different tasks, indicating its potential for widespread application in the development of non-collaborative agents.
Conclusion
METRO represents a significant advancement in the field of dialogue systems, providing a scalable and efficient method for strategy induction. By leveraging the capabilities of large language models and structuring expert knowledge in a meaningful way, METRO opens new avenues for creating sophisticated non-collaborative agents. For those interested in exploring METRO further, the code is available at https://github.com/Humphrey-0125/METRO.