Does Rationale Quality Matter? Enhancing Mental Disorder Detection via Selective Reasoning Distillation

Understand

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.