METRO: Towards Strategy Induction from Expert Dialogue Transcripts for Non-collaborative Dialogues
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915940168368128 |
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| author | Yang, Haofu Liu, Jiaji Huang, Chen Wu, Faguo Lei, Wenqiang Ng, See-Kiong |
| author_facet | Yang, Haofu Liu, Jiaji Huang, Chen Wu, Faguo Lei, Wenqiang Ng, See-Kiong |
| contents | Developing non-collaborative dialogue agents traditionally requires the manual, unscalable codification of expert strategies. We propose \ours, 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). 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. Our code is available at https://github.com/Humphrey-0125/METRO. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_11427 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | METRO: Towards Strategy Induction from Expert Dialogue Transcripts for Non-collaborative Dialogues Yang, Haofu Liu, Jiaji Huang, Chen Wu, Faguo Lei, Wenqiang Ng, See-Kiong Computation and Language Artificial Intelligence Developing non-collaborative dialogue agents traditionally requires the manual, unscalable codification of expert strategies. We propose \ours, 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). 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. Our code is available at https://github.com/Humphrey-0125/METRO. |
| title | METRO: Towards Strategy Induction from Expert Dialogue Transcripts for Non-collaborative Dialogues |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2604.11427 |