@inproceedings{hoang-etal-2026-llms,
title = "{LLM}s as Standardised Patients for Motivational Interviewing: How Faithful Are They?",
author = "Hoang, Van and
Rogers, Eoin and
Ross, Robert",
editor = "Zirikly, Aya and
Bar, Kfir and
MacAvaney, Sean and
Ireland, Molly and
Ophir, Yaakov and
Atzil-Slonim, Dana and
Varadarajan, Vasudha and
Bedrick, Steven and
Desmet, Bart",
booktitle = "Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology ({CLP}sych 2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.clpsych-1.21/",
pages = "258--270",
ISBN = "979-8-89176-421-7",
abstract = "Recent advances in large language models (LLMs) have enabled the creation of highly realistic digital patients across a broad range of clinical scenarios, yet systematic evaluation of such simulations remains challenging due to a lack of standardised methodology. This paper investigates the faithfulness of LLM-simulated patients within motivational interviewing contexts. We directly compare the properties of data generated by simulated and human patients given identical profiles, rather than relying on subjective user experiences. Our findings reveal that while simulated and human patients produce semantically similar content and engage with comparable topics, their modes of expression differ substantially. LLM-simulated patients struggle to reproduce the full complexity of human behaviours and attitudes. While human patients exhibit a mix of positive and negative responses, LLM patients skew toward uniformly ones."
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<abstract>Recent advances in large language models (LLMs) have enabled the creation of highly realistic digital patients across a broad range of clinical scenarios, yet systematic evaluation of such simulations remains challenging due to a lack of standardised methodology. This paper investigates the faithfulness of LLM-simulated patients within motivational interviewing contexts. We directly compare the properties of data generated by simulated and human patients given identical profiles, rather than relying on subjective user experiences. Our findings reveal that while simulated and human patients produce semantically similar content and engage with comparable topics, their modes of expression differ substantially. LLM-simulated patients struggle to reproduce the full complexity of human behaviours and attitudes. While human patients exhibit a mix of positive and negative responses, LLM patients skew toward uniformly ones.</abstract>
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%0 Conference Proceedings
%T LLMs as Standardised Patients for Motivational Interviewing: How Faithful Are They?
%A Hoang, Van
%A Rogers, Eoin
%A Ross, Robert
%Y Zirikly, Aya
%Y Bar, Kfir
%Y MacAvaney, Sean
%Y Ireland, Molly
%Y Ophir, Yaakov
%Y Atzil-Slonim, Dana
%Y Varadarajan, Vasudha
%Y Bedrick, Steven
%Y Desmet, Bart
%S Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-421-7
%F hoang-etal-2026-llms
%X Recent advances in large language models (LLMs) have enabled the creation of highly realistic digital patients across a broad range of clinical scenarios, yet systematic evaluation of such simulations remains challenging due to a lack of standardised methodology. This paper investigates the faithfulness of LLM-simulated patients within motivational interviewing contexts. We directly compare the properties of data generated by simulated and human patients given identical profiles, rather than relying on subjective user experiences. Our findings reveal that while simulated and human patients produce semantically similar content and engage with comparable topics, their modes of expression differ substantially. LLM-simulated patients struggle to reproduce the full complexity of human behaviours and attitudes. While human patients exhibit a mix of positive and negative responses, LLM patients skew toward uniformly ones.
%U https://aclanthology.org/2026.clpsych-1.21/
%P 258-270
Markdown (Informal)
[LLMs as Standardised Patients for Motivational Interviewing: How Faithful Are They?](https://aclanthology.org/2026.clpsych-1.21/) (Hoang et al., CLPsych 2026)
ACL