InterPrompt: Interpretable Prompting for Interrelated Interpersonal Risk
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MSVPJ Sathvik Surjodeep Sarkar Chandni Saxena Sunghwan Sohn Muskan Garg
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.