Words Caught in the Heart: Simulating Person-Specific Expression for Self-Opaque Clients in Psychotherapy
Words Caught in the Heart: Simulating Person-Specific Expression for Self-Opaque Clients in Psychotherapy
The Pandora framework generates more realistic and individualized customer expressions by modeling the evolution of psychological and relational states, reducing AI-like responses. In an evaluation of 115 cases, Pandora outperformed five baseline systems in expression similarity and disclosure consistency scores, with only 7.2% of 45 disclosures rated as AI-like expressions, significantly lower than the baseline systems' 43.3--67.2%.
Published on Oct 31
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Abstract
Pandora is a psychotherapy client simulation framework that models evolving psychological and relational states to generate more authentic, individually aligned expressions and reduce AI-like responses.
Psychotherapy clients may experience distress before they can coherently describe it, and their expression changes with emotion, perceived safety, and trust. Large language model-based client simulators often rely on clinically organized case descriptions, which can encourage responses explaining the client's difficulties more clearly than their current understanding warrants. We propose Pandora, a psychotherapy client simulation framework that models how psychological experience becomes situated, individual expression. Pandora updates psychological and relational states in response to therapist interventions, constructs subjective appraisals and attributions, and plans response content and strategy. A separate expression planner combines these decisions with language patterns distilled from the client's utterances to guide linguistic realization. We evaluate Pandora against five baselines on 115 cases from four counseling and motivational interviewing datasets. It achieves the highest expression similarity and overall disclosure-alignment score among the compared systems. In a four-evaluator study of 45 matched disclosure items, Pandora receives AI-like judgments in 7.2% of ratings, compared with 43.3--67.2% for the baselines and 35.6% without expression planning. The ablation also reduces expression similarity. These results support explicit expression planning in client simulation to improve alignment with individual language patterns and reduce perceived AI-like expression in the evaluated setting.
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