aiqbench analysis
The paper presents PsyInterviewee, an LLM-based simulated patient for mental health interviewer training. Its main contribution is separating a protected channel for profile-grounded clinical facts from a dynamic channel for disclosure and behavior, so that human-likeness isn't the only measure of reliability. Results come from automated evaluation with two LLM judges and ablations, not from human clinicians, so the reliability claims need validation before deployment. Readers working on LLM evaluation and grounding will find the asymmetric write-permission design relevant.
PsyInterviewee: Informational Invariance and Relational Adaptivity in Mental Health Interview Simulation
arXiv:2610.41289v1 Announce Type: new Abstract: LLM-based simulated interviewees support interviewer training and the evaluation of mental health interviewing systems. Existing studies have primarily focused on improving human-likeness through richer patient profiles and behavioral modeling. However, human-likeness alone does not establish their reliability. Reliable simulation requires preserving profile-grounded clinical facts (informational invariance) while adapting disclosure and interaction behavior to the interview (relational adaptivity). To this end, we introduce PsyInterviewee, which separates a protected truth channel from a dynamic interaction channel and verifies generated clinical claims against the profile. Specifically, asymmetric write permissions allow dialogue context to update disclosure and behavioral states without authorizing new clinical facts; these states guide what profile-supported information is revealed and how it is expressed. In an automated evaluation of interactions between LLM-based interviewers and interviewees, PsyInterviewee achieves higher scores than four representative baselines on most metrics across both axes, as assessed by two LLM judges. Ablations suggest that the two channels contribute to clinical grounding and response modulation, respectively.
Abstract:LLM-based simulated interviewees support interviewer training and the evaluation of mental health interviewing systems. Existing studies have primarily focused on improving human-likeness through richer patient profiles and behavioral modeling. However, human-likeness alone does not establish their reliability. Reliable simulation requires preserving profile-grounded clinical facts (informational invariance) while adapting disclosure and interaction behavior to the interview (relational adaptivity). To this end, we introduce PsyInterviewee, which separates a protected truth channel from a dynamic interaction channel and verifies generated clinical claims against the profile. Specifically, asymmetric write permissions allow dialogue context to update disclosure and behavioral states without authorizing new clinical facts; these states guide what profile-supported information is revealed and how it is expressed. In an automated evaluation of interactions between LLM-based interviewers and interviewees, PsyInterviewee achieves higher scores than four representative baselines on most metrics across both axes, as assessed by two LLM judges. Ablations suggest that the two channels contribute to clinical grounding and response modulation, respectively.
38 pages, including appendices. Code: this https URL
Computation and Language (cs.CL)
arXiv:2610.41289 [cs.CL]
(or arXiv:2610.41289v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.41289 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 261 words