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Mental health professionals and clinicians have observed the upsurge ofmental disorders due to Interpersonal Risk Factors (IRFs). To simulate thehuman-in-the-loop triaging scenario for early detection of mental healthdisorders, we recognized textual indications to ascertain these IRFs : ThwartedBelongingness (TBe) and Perceived Burdensomeness (PBu) within personalnarratives. In light of this, we use N-shot learning with GPT-3 model on theIRF dataset, and underscored the importance of fine-tuning GPT-3 model toincorporate the context-specific sensitivity and the interconnectedness oftextual cues that represent both IRFs.  In this paper, we introduce an Interpretable Prompting (InterPrompt)} methodto boost the attention mechanism by fine-tuning the GPT-3 model. This allows amore sophisticated level of language modification by adjusting the pre-trainedweights. Our model learns to detect usual patterns and underlying connectionsacross both the IRFs, which leads to better system-level explainability andtrustworthiness. The results of our research demonstrate that all four variantsof GPT-3 model, when fine-tuned with InterPrompt, perform considerably betteras compared to the baseline methods, both in terms of classification andexplanation generation.

InterPrompt: Interpretable Prompting for Interrelated Interpersonal Risk Factors in Reddit Posts