10 citations · 26 across the 10 of their papers we have counts for
13 papers
Aligning with Lived Experience: Heterogeneous Benefits of Fine Tuning in Mental Health Support Generation
Mohit Chandra, Nabin Kim, Eli Min +3
As access to professional mental healthcare remains limited, many individuals turn to online platforms such as Reddit to seek peer support situated within human lived experience. H…
CliniCIRCA: A Modular LLM Framework for Constructing Longitudinal Mental Health Patient Journeys from Raw EHR Narratives
Aiwei Ivy Zhang, Nimra Ishfaq, Mohit Chandra +5
In mental health care, reasoning over patient journeys is a key task for clinicians. Yet these journeys, encompassing a longitudinal progression of biological, psychological, and s…
Reasoning Is Not All You Need: Examining LLMs for Multi-Turn Mental Health Conversations
Mohit Chandra, Siddharth Sriraman, Harneet Singh Khanuja +2
Limited access to mental healthcare, extended wait times, and increasing capabilities of Large Language Models (LLMs) has led individuals to turn to LLMs for fulfilling their menta…
Longitudinal Study on Social and Emotional Use of AI Conversational Agent
Mohit Chandra, Javier Hernandez, Gonzalo Ramos +7
Development in digital technologies has continuously reshaped how individuals seek and receive social and emotional support. While online platforms and communities have long served…
From Lived Experience to Insight: Unpacking the Psychological Risks of Using AI Conversational Agents
Mohit Chandra, Suchismita Naik, Denae Ford +8
Recent gains in popularity of AI conversational agents have led to their increased use for improving productivity and supporting well-being. While previous research has aimed to un…
Lived Experience Not Found: LLMs Struggle to Align with Experts on Addressing Adverse Drug Reactions from Psychiatric Medication Use
Mohit Chandra, Siddharth Sriraman, Gaurav Verma +5
Adverse Drug Reactions (ADRs) from psychiatric medications are the leading cause of hospitalizations among mental health patients. With healthcare systems and online communities fa…
MedHalu: Hallucinations in Responses to Healthcare Queries by Large Language Models
Vibhor Agarwal, Yiqiao Jin, Mohit Chandra +3
Large language models (LLMs) are starting to complement traditional information seeking mechanisms such as web search. LLM-powered chatbots like ChatGPT are gaining prominence amon…
Better to Ask in English: Cross-Lingual Evaluation of Large Language Models for Healthcare Queries
Yiqiao Jin, Mohit Chandra, Gaurav Verma +3
Large language models (LLMs) are transforming the ways the general public accesses and consumes information. Their influence is particularly pronounced in pivotal sectors like heal…
ReadMe++: Benchmarking Multilingual Language Models for Multi-Domain Readability Assessment
Tarek Naous, Michael J. Ryan, Anton Lavrouk +2
We present a comprehensive evaluation of large language models for multilingual readability assessment. Existing evaluation resources lack domain and language diversity, limiting t…
"A Virus Has No Religion": Analyzing Islamophobia on Twitter During the COVID-19 Outbreak
Mohit Chandra, Manvith Reddy, Shradha Sehgal +3
The COVID-19 pandemic has disrupted people's lives driving them to act in fear, anxiety, and anger, leading to worldwide racist events in the physical world and online social netwo…
"Subverting the Jewtocracy": Online Antisemitism Detection Using Multimodal Deep Learning
Mohit Chandra, Dheeraj Pailla, Himanshu Bhatia +4
The exponential rise of online social media has enabled the creation, distribution, and consumption of information at an unprecedented rate. However, it has also led to the burgeon…
AbuseAnalyzer: Abuse Detection, Severity and Target Prediction for Gab Posts
Mohit Chandra, Ashwin Pathak, Eesha Dutta +4
While extensive popularity of online social media platforms has made information dissemination faster, it has also resulted in widespread online abuse of different types like hate…
Exact Passive-Aggressive Algorithms for Learning to Rank Using Interval Labels
Naresh Manwani, Mohit Chandra
In this paper, we propose exact passive-aggressive (PA) online algorithms for learning to rank. The proposed algorithms can be used even when we have interval labels instead of act…