Does Rationale Quality Matter? Enhancing Mental Disorder Detection via Selective Reasoning Distillation
Journal:
arXiv
Published Date:
May 26, 2025
Abstract
The detection of mental health problems from social media and the
interpretation of these results have been extensively explored. Research has
shown that incorporating clinical symptom information into a model enhances
domain expertise, improving its detection and interpretation performance. While
large language models (LLMs) are shown to be effective for generating
explanatory rationales in mental health detection, their substantially large
parameter size and high computational cost limit their practicality. Reasoning
distillation transfers this ability to smaller language models (SLMs), but
inconsistencies in the relevance and domain alignment of LLM-generated
rationales pose a challenge. This paper investigates how rationale quality
impacts SLM performance in mental health detection and explanation generation.
We hypothesize that ensuring high-quality and domain-relevant rationales
enhances the distillation. To this end, we propose a framework that selects
rationales based on their alignment with expert clinical reasoning. Experiments
show that our quality-focused approach significantly enhances SLM performance
in both mental disorder detection and rationale generation. This work
highlights the importance of rationale quality and offers an insightful
framework for knowledge transfer in mental health applications.
