What Users Cannot See: Evaluating LLM Emotional Support Beyond User Preference
Abstract
People increasingly turn to LLMs for emotional support, yet common evaluations reward responses that feel helpful and may miss consequential response behaviors. We introduce a theory-informed measurement framework that decomposes LLM emotional-support responses into Soothe (affective comfort), Reframe (cognitive perspective-shift), and Endorse (agreement with a user's causal or moral framing). Across 9,000 GPT-5.6 responses to 3,000 venting and advice-seeking Reddit posts, friend- and therapist-style personas both increased Soothe relative to default but moved Reframe and Endorse in opposite directions: friend prompting increased Endorse and reduced Reframe, a pattern that could reinforce users' existing appraisals and plausibly contribute to escalation risk, whereas therapist prompting did the reverse. Two independent LLM judges substantially agreed with clinically trained raters, yet lay raters detected only 60% of expert-confirmed Endorse instances and rated the personas similarly helpful and desirable. Preference-based evaluation thus has a blind spot: supportive presentation can obscure appraisal-reinforcing, potentially escalatory behavior.
