Distinct Profiles of Run-to-Run Score Reliability and Expert-Panel Alignment Across Four LLM Evaluators of Simulated Japanese-Language AI-to-AI Counseling

Abstract

Large language models (LLMs) increasingly evaluate generated dialogue, but repeatable scores do not necessarily align with professional judgment. This observational fixed-benchmark study compared four configured LLM evaluator systems (GPT-5.5, Gemini 3.5 Flash, Claude Opus 4.8, and Fable 5) with aggregated ratings from 15 counseling experts on 18 complete simulated AI-to-AI counseling sessions conducted in Japanese. The sessions represented three counselor conditions across six prespecified client profiles. Each system scored every transcript three times on four motivational interviewing-informed dimensions and overall quality. All four systems assigned higher scores than the expert panel for softening sustain talk and overall quality, although differences varied across systems and constructs. Single-run intraclass correlation coefficients ranged from .33 to .96, showing that high run-to-run reliability did not ensure closer expert-panel alignment. Claude Opus 4.8 had the smallest mean absolute difference, whereas Fable 5 had an intermediate difference. In a secondary benchmark analysis, GPT-4-turbo sessions generated with the Structured Multi-step Dialogue Prompt received higher expert ratings than sessions generated by the same model with a minimal instruction for cultivating change talk, partnership, empathy, and overall quality; the softening sustain talk contrast remained uncertain. The fixed benchmark contained one session per counselor-condition-by-profile cell, so inference concerns these sessions rather than all possible stochastic regenerations. Run-to-run reliability, expert-panel alignment, and condition discrimination are separate properties of automated counseling evaluation. This benchmark supports construct-level assessment of LLM evaluators against professional judgment.