Papers by Vasudha Varadarajan
Transfer and Active Learning for Dissonance Detection: Addressing the Rare-Class Challenge (2023.acl-long)
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Vasudha Varadarajan, Swanie Juhng, Syeda Mahwish, Xiaoran Liu, Jonah Luby, Christian Luhmann, H. Andrew Schwartz
| Challenge: | Active learning has been proposed to alleviate data acquisition challenges for rare-class tasks when the class label is very infrequent (e.g., 5% of samples). |
| Approach: | They propose to use transformers to train models on closely related tasks and evaluate acquisition strategies, including a proposed probability-of-rare-class approach to dissonance detection. |
| Outcome: | The proposed method improves model accuracy while iterative transfer-learning does not improve cold-start performance. |
From Word Sequences to Behavioral Sequences: Adapting Modeling and Evaluation Paradigms for Longitudinal NLP (2026.acl-long)
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Adithya V Ganesan, Vasudha Varadarajan, Oscar Kjell, Whitney Ringwald, Scott M. Feltman, Benjamin J. Luft, Roman Kotov, Ryan L. Boyd, H. Andrew Schwartz
| Challenge: | a longitudinal model for NLP relies on document-level evaluation to map isolated instances of language to an outcome. |
| Approach: | They propose a longitudinal model that aligns evaluation splits to generalization over people and time . they propose integrating a sequence inputs to incorporate history by default . |
| Outcome: | The proposed model improves on a dataset of 17k daily diary transcripts paired with PTSD symptom severity from 238 participants. |
ALBA: Adaptive Language-Based Assessments for Mental Health (2024.naacl-long)
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| Challenge: | Adaptive language-based assessments require a substantial sample of words per person for accuracy. |
| Approach: | They propose an adaptive language-based assessment task that involves ordering questions and scoring latent psychological trait using limited language responses to previous questions. |
| Outcome: | The proposed methods improve over non-adaptive baselines, but are more accurate and scalable with fewer questions. |
MAQuA: Multi-outcome Adaptive Question-Asking for Mental Health using Item Response Theory (2026.eacl-long)
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Vasudha Varadarajan, Hui Xu, Rebecca Astrid Böhme, Mariam Marlen Mirström, Sverker Sikström, H. Andrew Schwartz
| Challenge: | Evaluations of large language models (LLMs) indicate that such assessments are inconsistent and in many cases less accurate than dedicated condition-specific models with established psychometric validity. |
| Approach: | They propose a multi-outcome modeling and adaptive question-asking framework for simultaneous, multidimensional mental health screening that integrates language responses with item response theory and factor analysis. |
| Outcome: | Empirical results show that MAQuA reduces the number of assessment questions required for score stabilization by 50–87% compared to random ordering. |
Systematic Evaluation of Auto-Encoding and Large Language Model Representations for Capturing Author States and Traits (2025.findings-acl)
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Khushboo Singh, Vasudha Varadarajan, Adithya V Ganesan, August Håkan Nilsson, Nikita Soni, Syeda Mahwish, Pranav Chitale, Ryan L. Boyd, Lyle Ungar, Richard N Rosenthal, H. Schwartz
| Challenge: | Large Language Models (LLMs) are increasingly used in human-centered applications, yet their ability to model diverse psychological constructs is not well understood. |
| Approach: | They evaluated a range of Transformer-LMs to predict psychological variables across five major dimensions: affect, substance use, mental health, sociodemographics, and personality. |
| Outcome: | The models predict affect, substance use, mental health, sociodemographics, and personality across five major dimensions. |
Discourse-Level Representations can Improve Prediction of Degree of Anxiety (2023.acl-short)
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Swanie Juhng, Matthew Matero, Vasudha Varadarajan, Johannes Eichstaedt, Adithya V Ganesan, H. Andrew Schwartz
| Challenge: | Anxiety disorders are the most common of mental illnesses, but little is known about how to detect them from language. |
| Approach: | They propose to use discourse-level information in addition to lexical-level large language model embeddings to evaluate the utility of a lexico-discourse model. |
| Outcome: | The proposed model outperforms models based on state-of-the-art contextual embeddings and uses discourse patterns of causal explanations significantly more than models derived from Sentence-BERT and DiscRE, and is comparable to psychological models. |
From Text to Context: Contextualizing Language with Humans, Groups, and Communities for Socially Aware NLP (2024.naacl-tutorials)
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Adithya V Ganesan, Siddharth Mangalik, Vasudha Varadarajan, Nikita Soni, Swanie Juhng, João Sedoc, H. Andrew Schwartz, Salvatore Giorgi, Ryan L Boyd
| Challenge: | This tutorial will cover the latest techniques and libraries for doing so at each level of analysis. |
| Approach: | This tutorial will cover the latest techniques and libraries for doing so at each level of analysis. |
| Outcome: | The tutorial covers human-centered techniques that provide benefit to traditional document- or word-level NLP tasks. |
Examining the Utility of Self-disclosure Types for Modeling Annotators of Social Norms (2026.findings-eacl)
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| Challenge: | Recent work has explored the use of personal information in the form of persona sentences to improve modeling of individual characteristics and prediction of annotator labels for subjective tasks. |
| Approach: | They categorize self-disclosures and use them to build annotator models for predicting judgments of social norms by analyzing comments from original post. |
| Outcome: | The proposed model improves the model and its ability to predict annotator labels. |
Capturing Human Cognitive Styles with Language: Towards an Experimental Evaluation Paradigm (2025.naacl-short)
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Vasudha Varadarajan, Syeda Mahwish, Xiaoran Liu, Julia Buffolino, Christian Luhmann, Ryan L. Boyd, H. Schwartz
| Challenge: | While NLP models often capture cognitive states via language, validity of predicted states is determined by comparing annotations created without access to the cognitive states of the authors. |
| Approach: | They propose a framework for evaluating language-based cognitive style models against human behavior by using an experiment-based framework. |
| Outcome: | The proposed framework shows that language features can predict participants’ decision style with moderate-to-high accuracy (AUC 0.8), demonstrating that cognitive style can be partly captured and revealed by discourse patterns. |