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cs.CL2024★ 4 cited
Still Not Quite There! Evaluating Large Language Models for Comorbid Mental Health Diagnosis
Amey Hengle, Atharva Kulkarni, Shantanu Patankar +4
In this study, we introduce ANGST, a novel, first-of-its kind benchmark for depression-anxiety comorbidity classification from social media posts. Unlike contemporary datasets that…
cs.CL2024
SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State Tracking
Atharva Kulkarni, Bo-Hsiang Tseng, Joel Ruben Antony Moniz +3
In-context learning with Large Language Models (LLMs) has emerged as a promising avenue of research in Dialog State Tracking (DST). However, the best-performing in-context learning…