Papers by Mohit Chandra
AbuseAnalyzer: Abuse Detection, Severity and Target Prediction for Gab Posts (2020.coling-main)
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Mohit Chandra, Ashwin Pathak, Eesha Dutta, Paryul Jain, Manish Gupta, Manish Shrivastava, Ponnurangam Kumaraguru
| Challenge: | Existing studies on estimating the severity of abuse and the target of online abuse have focused on detecting and curtailment of such types of abuse. |
| Approach: | They propose to analyze online abuse from the perspective of presence, severity and target of abusive behavior from 7,601 posts from Gab and to estimate the severity of abuse. |
| Outcome: | The proposed system achieves 80% accuracy for abuse presence, 82% accuracy for abusive target prediction, and 65% accuracy for severity prediction. |
Lived Experience Not Found: LLMs Struggle to Align with Experts on Addressing Adverse Drug Reactions from Psychiatric Medication Use (2025.naacl-long)
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Mohit Chandra, Siddharth Sriraman, Gaurav Verma, Harneet Singh Khanuja, Jose Suarez Campayo, Zihang Li, Michael L. Birnbaum, Munmun De Choudhury
| Challenge: | Adverse Drug Reactions (ADRs) from psychiatric medications are the leading cause of hospitalizations among mental health patients. |
| Approach: | They propose a benchmark and a framework to evaluate LLMs' ability to detect ADRs . they find that LLM responses are more complex and harder to read than experts . |
| Outcome: | The proposed framework evaluates LLMs' ability to detect and deliver expert-aligned mitigation strategies. |
Reasoning Is Not All You Need: Examining LLMs for Multi-Turn Mental Health Conversations (2026.acl-long)
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| Challenge: | Existing evaluation frameworks focus on diagnostic accuracy and win-rates and often overlook alignment with patient-specific goals, values, and personalities required for meaningful conversations. |
| Approach: | They propose a framework for synthetically generating realistic, multi-turn mental health sensemaking conversations and a dataset to examine their models in healthcare settings. |
| Outcome: | The proposed framework synthesizes a dataset comprising over 2,200 patient–LLM conversations and evaluates them using human-centric criteria. |
ReadMe++: Benchmarking Multilingual Language Models for Multi-Domain Readability Assessment (2024.emnlp-main)
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| Challenge: | Existing evaluation resources lack domain and language diversity, limiting the ability for cross-domain and cross-lingual analyses. |
| Approach: | They propose to use a multilingual multi-domain dataset to benchmark multilingual and monolingual models for multilingual readability assessment. |
| Outcome: | The proposed model trains better in supervised, unsupervised, and few-shot prompting settings and identifies shortcomings in state-of-the-art unsupervised methods. |