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StoryMI: Steerable Multi-Agent Therapeutic Dialogue Generation

Paper · 2026 · Qingyu Meng, Min Chen, Dingming Liu et al.

Summary

StoryMI targets three weaknesses of LLM-generated motivational interviewing (MI) dialogues: client personas lack concrete situational context, counseling strategy cannot be steered across turns, and standard metrics overlook clinical strategy. The framework first builds client profiles from the DSM-5 cross-cutting symptom questionnaire, where an LLM assigns item severities and short first-person rationales. It then turns each profile into a roughly 200-word situational story. Three agents run the conversation. Client and therapist agents produce utterances tagged with MI codes. An interaction manager tracks a shared dialogue state, chooses the therapist's next MI code based on the recent code history, and decides when the session ends. The evaluation combines lexical metrics, MI-specific measures of macro-level strategy, LLM judges and expert ratings. The authors report that story grounding and strategy-level control improve MI adherence and clinical plausibility. They also find that LLM judges handle structural qualities well but miss empathy and naturalness. This motivates hybrid evaluation and offers a reproducible resource for research on psychotherapy dialogue.

Key points

  • The released dataset contains 6,000 simulated MI dialogues built from 1,000 questionnaire–story pairs, covering 12 MI codes and 13 DSM-5 symptom domains (23 questionnaire items). Code and data are on GitHub (Beren-sds/StoryMI).
  • Six open- and closed-source LLMs are benchmarked within the same multi-agent workflow.
  • Two LLM judges, GLM-5 and GPT-5-Nano, were compared against human ratings. GLM-5 correlated better with humans on 4 of 6 dimensions, for example Progress (r ≈ 0.54 vs. 0.43). On Naturalness, GPT-5-Nano's correlation was near zero (r ≈ 0.01), while GLM-5's was weak but significant (ρ ≈ 0.22).
  • Models that asked more open questions received higher human empathy ratings. Different strategy profiles, either question-heavy or reflection-heavy, reached comparable human-rated quality.
  • Stated limitations:
    • The work covers MI only.
    • Validation relies on expert annotation, with no real clients involved.
    • DSM-5 profiling carries a Western bias and limits persona diversity.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Tokyo Institute of TechnologyTokyo · Japan22 works · 2,137 citations🎓Vrije Universiteit AmsterdamAmsterdam · Netherlands42 works · 3,372 citations

 Fields, topics and concepts

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksPsychology · 1,552 worksExperimental and Cognitive Psychology · 870 worksApplied Psychology · 21 works

🧭 Topics 1Research subjects and languages the work is about.Dialogue systems · 624 works

Cite

Cite as Qingyu Meng, Min Chen, Dingming Liu, Yifan Mo, Yue Su, Xin Sun, Koen Hindriks and Jiahuan Pei. 2026. StoryMI: Steerable Multi-Agent Therapeutic Dialogue Generation. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.468

@inproceedings{meng2026storymi,
  title = {{StoryMI: Steerable Multi-Agent Therapeutic Dialogue Generation}},
  author = {Qingyu Meng, Min Chen, Dingming Liu, Yifan Mo, Yue Su, Xin Sun, Koen Hindriks and Jiahuan Pei},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.468},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.468}
}

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  44. A Definition. Given a sequence of therapist MI codes c = (c 1 , . . . , c n ) where each c i ∈ C = {reflection, question, input, other}, we compute: H(c) = − X 1 p(c) log 2 p(c) log 2 \|C obs \| find

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