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arXiv · 2601.07698

Stress-Testing Emotional Support Models: Moving from Homogeneous to Diverse Help Seekers

Chaewon Heo·Cheyon Jin·Yohan Jo
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Abstract

As emotional support chatbots have recently gained significant traction across both research and industry, a common evaluation strategy has emerged: use help-seeker simulators to interact with supporter chatbots. However, current simulators suffer from two critical limitations: (1) they fail to capture the behavioral diversity of real-world seekers, often portraying them as overly cooperative, and (2) they lack the controllability required to simulate specific seeker profiles. To address these challenges, we present a controllable seeker simulator driven by nine psychological and linguistic features that underpin seeker behavior. Using authentic Reddit conversations, we train our model via a Mixture-of-Experts (MoE) architecture, which effectively differentiates diverse seeker behaviors into specialized parameter subspaces, thereby enhancing fine-grained controllability. Our simulator achieves superior profile adherence and behavioral diversity compared to existing approaches. Furthermore, evaluating 7 prominent supporter models with our system uncovers previously obscured performance degradations. These findings underscore the utility of our framework in providing a more faithful and stress-tested evaluation for emotional support chatbots.

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Chaewon Heo, Cheyon Jin, Yohan Jo. 2026-04-25. Stress-Testing Emotional Support Models: Moving from Homogeneous to Diverse Help Seekers. https://arxiv.org/abs/2601.07698

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