  Mental health professionals and clinicians have observed the upsurge of
mental disorders due to Interpersonal Risk Factors (IRFs). To simulate the
human-in-the-loop triaging scenario for early detection of mental health
disorders, we recognized textual indications to ascertain these IRFs : Thwarted
Belongingness (TBe) and Perceived Burdensomeness (PBu) within personal
narratives. In light of this, we use N-shot learning with GPT-3 model on the
IRF dataset, and underscored the importance of fine-tuning GPT-3 model to
incorporate the context-specific sensitivity and the interconnectedness of
textual cues that represent both IRFs.
  In this paper, we introduce an Interpretable Prompting (InterPrompt)} method
to boost the attention mechanism by fine-tuning the GPT-3 model. This allows a
more sophisticated level of language modification by adjusting the pre-trained
weights. Our model learns to detect usual patterns and underlying connections
across both the IRFs, which leads to better system-level explainability and
trustworthiness. The results of our research demonstrate that all four variants
of GPT-3 model, when fine-tuned with InterPrompt, perform considerably better
as compared to the baseline methods, both in terms of classification and
explanation generation.

Computation and Language;Information Retrieval