PsychePass: Calibrating LLM Therapeutic Competence via Trajectory-Anchored Tournaments
This work addresses the problem of reliably assessing AI models for mental healthcare applications, though it appears incremental as it builds on existing evaluation paradigms.
The paper tackles the challenge of evaluating large language models' therapeutic competence in mental healthcare by addressing instability in current methods, and introduces PsychePass, a framework that uses trajectory-anchored tournaments to calibrate and improve LLM performance, validated through experiments showing strong consistency with human expert judgments.
While large language models show promise in mental healthcare, evaluating their therapeutic competence remains challenging due to the unstructured and longitudinal nature of counseling. We argue that current evaluation paradigms suffer from an unanchored defect, leading to two forms of instability: process drift, where unsteered client simulation wanders away from specific counseling goals, and standard drift, where static pointwise scoring lacks the stability for reliable judgment. To address this, we introduce Ps, a unified framework that calibrates the therapeutic competence of LLMs via trajectory-anchored tournaments. We first anchor the interaction trajectory in simulation, where clients precisely control the fluid consultation process to probe multifaceted capabilities. We then anchor the battle trajectory in judgments through an efficient Swiss-system tournament, utilizing dynamic pairwise battles to yield robust Elo ratings. Beyond ranking, we demonstrate that tournament trajectories can be transformed into credible reward signals, enabling on-policy reinforcement learning to enhance LLMs' performance. Extensive experiments validate the effectiveness of PsychePass and its strong consistency with human expert judgments.