Paper Number

ICIS2026-1753

Paper Type

Short

Abstract

Mental health assessment in social media is challenging because psychological expressions are implicit, heterogeneous, and difficult to interpret using purely data-driven models. While integrating domain knowledge has emerged as a primary strategy, existing approaches largely rely on static knowledge bases that fragment holistic clinical reasoning into rigid fact entries, limiting their ability to foster domain-specific reasoning. To address this challenge, we propose InfoPsych-MoE, an information-theoretic case-based reasoning mixture-of-experts framework that enables LLMs to acquire and coordinate domain-specific reasoning expertise. Specifically, we develop a Case-Based Reasoning Internalized Prompting (CBRIP) pipeline to construct transferable reasoning patterns through the Retrieve–Reuse–Revise–Retain cycle. We further introduce a PMI-based dual-level utility assessment mechanism that evaluates the contribution of reasoning patterns, guiding reasoning refinement and expert routing. Experiments on three mental health assessment tasks demonstrate that InfoPsych-MoE achieves superior performance and provides an effective framework for adaptive mental health assessment.

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Dec 14th, 12:00 AM

InfoPsych-MoE: An Information-Theoretic Case-Based Reasoning Mixture-of-Experts Framework for Mental Health Detection

Mental health assessment in social media is challenging because psychological expressions are implicit, heterogeneous, and difficult to interpret using purely data-driven models. While integrating domain knowledge has emerged as a primary strategy, existing approaches largely rely on static knowledge bases that fragment holistic clinical reasoning into rigid fact entries, limiting their ability to foster domain-specific reasoning. To address this challenge, we propose InfoPsych-MoE, an information-theoretic case-based reasoning mixture-of-experts framework that enables LLMs to acquire and coordinate domain-specific reasoning expertise. Specifically, we develop a Case-Based Reasoning Internalized Prompting (CBRIP) pipeline to construct transferable reasoning patterns through the Retrieve–Reuse–Revise–Retain cycle. We further introduce a PMI-based dual-level utility assessment mechanism that evaluates the contribution of reasoning patterns, guiding reasoning refinement and expert routing. Experiments on three mental health assessment tasks demonstrate that InfoPsych-MoE achieves superior performance and provides an effective framework for adaptive mental health assessment.

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