activity
20202026
most citedExplaining GPTs' Schema of Depression: A Machine Behavior Analysis

2 citations · 6 across the 19 of their papers we have counts for

collaborators

19 papers

cs.CV2026

NAMESAKES: Probing Identity Memorization in Text-to-Image Models

Morris Alper, Vasudha Varadarajan, Moran Yanuka +2

Text-to-image (T2I) models generate realistic likenesses of some individuals when prompted with their names, raising privacy concerns. However, distinguishing whether a generated f…

cs.CL2026

Evaluating Document-Tuned Transformer Representations for Person-level Mental Health Assessment

Aaron Marker, Oscar Kjell, Vasudha Varadarajan +1

Person-level psychological assessment requires aggregating meaning across many messages from the same individual, a task that document-level training objectives were not explicitly…

cs.CY2026

CCBENCH: Assessing LLM Cultural Competence via Implicitly Signaled Norms using Health Queries

Vasudha Varadarajan, Akhila Yerukola, Mona T. Diab +1

To interact with users fairly and without stereotyping, AI models must display cultural competency, i.e., the ability to infer and adapt to a user's implicitly signaled cultural va…

cs.CL2026

Lost in Delusion: Examining LLM Safety Under User Delusions and Distress

Andrew Aquilina, Chetna Nihalani, Vasudha Varadarajan +3

LLM chatbots increasingly serve as a first source of support for people in psychological distress, including those whose distress is entangled with delusional beliefs. Prior work o…

cs.CY2026

Public Perceptions and Acceptance of Socially Intelligent AI Agents

Leena Mathur, Jenny T. Liang, Vasudha Varadarajan +6

AI researchers have been advancing socially intelligent AI agents (Social-AI) across embodiments, from chatbots to physical robots. As Social-AI agents move from labs into everyday…

cs.MA2026

SOTOPIA-TOM: Evaluating Privacy and Information Management in Multi-Agent Interaction with Theory of Mind

Yashwanth YS, Ruichen Wang, Shihua Zeng +4

As LLM-based agents are increasingly interacting in multi-party settings, they need to properly handle information asymmetry, i.e., knowing when and to whom to disclose information…

cs.CL2026

Who We Are, Where We Are: Mental Health at the Intersection of Person, Situation, and Large Language Models

Nikita Soni, August Håkan Nilsson, Syeda Mahwish +3

Mental health is not a fixed trait but a dynamic process shaped by the interplay between individual dispositions and situational contexts. Building on interactionist and constructi…

cs.CL2026

Examining the Utility of Self-disclosure Types for Modeling Annotators of Social Norms

Kieran Henderson, Kian Omoomi, Vasudha Varadarajan +2

Recent work has explored the use of personal information in the form of persona sentences or self-disclosures to improve modeling of individual characteristics and prediction of an…

cs.CL2026

From Word Sequences to Behavioral Sequences: Adapting Modeling and Evaluation Paradigms for Longitudinal NLP

Adithya V Ganesan, Vasudha Varadarajan, Oscar NE Kjell +6

While NLP typically treats documents as independent and unordered samples, in longitudinal studies, this assumption rarely holds: documents are nested within authors and ordered in…

cs.CL2025

Critical or Compliant? The Double-Edged Sword of Reasoning in Chain-of-Thought Explanations

Eunkyu Park, Wesley Hanwen Deng, Vasudha Varadarajan +4

Explanations are often promoted as tools for transparency, but they can also foster confirmation bias; users may assume reasoning is correct whenever outputs appear acceptable. We…

cs.CL2025

ISCA: A Framework for Interview-Style Conversational Agents

Charles Welch, Allison Lahnala, Vasudha Varadarajan +4

We present a low-compute non-generative system for implementing interview-style conversational agents which can be used to facilitate qualitative data collection through controlled…

cs.CL2025★ 1 cited

MAQuA: Adaptive Question-Asking for Multidimensional Mental Health Screening using Item Response Theory

Vasudha Varadarajan, Hui Xu, Rebecca Astrid Boehme +3

Recent advances in large language models (LLMs) offer new opportunities for scalable, interactive mental health assessment, but excessive querying by LLMs burdens users and is inef…

cs.CL2025

Capturing Human Cognitive Styles with Language: Towards an Experimental Evaluation Paradigm

Vasudha Varadarajan, Syeda Mahwish, Xiaoran Liu +4

While NLP models often seek to capture cognitive states via language, the validity of predicted states is determined by comparing them to annotations created without access the cog…

cs.SI2025

Unifying the Extremes: Developing a Unified Model for Detecting and Predicting Extremist Traits and Radicalization

Allison Lahnala, Vasudha Varadarajan, Lucie Flek +2

The proliferation of ideological movements into extremist factions via social media has become a global concern. While radicalization has been studied extensively within the contex…

cs.CL2024★ 2 cited

Explaining GPTs' Schema of Depression: A Machine Behavior Analysis

Adithya V Ganesan, Vasudha Varadarajan, Yash Kumar Lal +9

Use of large language models such as ChatGPT (GPT-4/GPT-5) for mental health support has grown rapidly, emerging as a promising route to assess and help people with mood disorders…

cs.CL2023

ALBA: Adaptive Language-based Assessments for Mental Health

Vasudha Varadarajan, Sverker Sikström, Oscar N. E. Kjell +1

Mental health issues differ widely among individuals, with varied signs and symptoms. Recently, language-based assessments have shown promise in capturing this diversity, but they…

cs.CL2023★ 1 cited

Transfer and Active Learning for Dissonance Detection: Addressing the Rare-Class Challenge

Vasudha Varadarajan, Swanie Juhng, Syeda Mahwish +4

While transformer-based systems have enabled greater accuracies with fewer training examples, data acquisition obstacles still persist for rare-class tasks -- when the class label…

cs.CL2021★ 2 cited

Discourse Relation Embeddings: Representing the Relations between Discourse Segments in Social Media

Youngseo Son, Vasudha Varadarajan, H Andrew Schwartz

Discourse relations are typically modeled as a discrete class that characterizes the relation between segments of text (e.g. causal explanations, expansions). However, such predefi…

eess.SY2020

V-Formation via Model Predictive Control

Radu Grosu, Anna Lukina, Scott A. Smolka +3

We present recent results that demonstrate the power of viewing the problem of V-formation in a flock of birds as one of Model Predictive Control (MPC). The V-formation-MPC marriag…