Papers by Adithya V Ganesan
WhiSPA: Semantically and Psychologically Aligned Whisper with Self-Supervised Contrastive and Student-Teacher Learning (2025.acl-long)
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Rajath Rao, Adithya V Ganesan, Oscar Kjell, Jonah Luby, Akshay Raghavan, Scott M. Feltman, Whitney Ringwald, Ryan L. Boyd, Benjamin J. Luft, Camilo J. Ruggero, Neville Ryant, Roman Kotov, H. Schwartz
| Challenge: | Current speech encoding pipelines rely on an additional text-based LM to get robust representations of human communication, even though speech-to-text models often have a LM within. |
| Approach: | They propose to align Whisper's latent space with semantic representations from a text autoencoder and lexically derived embeddings of basic psychological dimensions: emotion and personality. |
| Outcome: | The proposed approach surpasses current speech encoders over self-supervised affective tasks and downstream psychological tasks, achieving an error reduction of 73.4% and 83.8%, respectively. |
SOCIALITE-LLAMA: An Instruction-Tuned Model for Social Scientific Tasks (2024.eacl-short)
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Gourab Dey, Adithya V Ganesan, Yash Kumar Lal, Manal Shah, Shreyashee Sinha, Matthew Matero, Salvatore Giorgi, Vivek Kulkarni, H. Schwartz
| Challenge: | Social science NLP tasks require large data to capture semantics and implicit pragmatics. |
| Approach: | They propose an open-source instruction tuning tool for social science NLP tasks that captures implicit pragmatic cues from text. |
| Outcome: | The proposed model matches or improves on a state-of-the-art, multi-task finetuned model on 80% of social tasks. |
Idiosyncratic Versus Normative Modeling of Atypical Speech Recognition: Dysarthric Case Studies (2025.emnlp-main)
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| Challenge: | Past studies have focused on fully personalized (or idiosyncratic) models for atypical speech . past studies focused on idiotic models, but current approaches focus on generalizing and handling idiomatic patterns . |
| Approach: | They compare four models that generalize and handle idiosyncrasy to find atypical speech . they find the dysarthric-idios-ync model performs better than the idioconic approach . |
| Outcome: | The proposed model generalizes and handles idiosyncrasy better than the idiocy model . the model requires less personalized data and reduces word error rate from 71% to 32% . |
Empirical Evaluation of Pre-trained Transformers for Human-Level NLP: The Role of Sample Size and Dimensionality (2021.naacl-main)
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| Challenge: | In human-level NLP tasks, the number of observations is often smaller than the standard 768+ hidden state sizes of each layer within transformer-based language models. |
| Approach: | They propose to use dimension reduction methods to fine-tune large models with limited data and to use pre-trained dimension reduction regimes to improve model performance. |
| Outcome: | The proposed model outperforms other models in human-level NLP tasks with a pre-trained dimension reduction regime. |
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. |
Capturing Author Self Beliefs in Social Media Language (2025.acl-long)
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Siddharth Mangalik, Adithya V Ganesan, Abigail B. Wheeler, Nicholas Kerry, Jeremy D. W. Clifton, H. Schwartz, Ryan L. Boyd
| Challenge: | Existing methods for identifying self beliefs are limited. |
| Approach: | They propose a task that classifies language that contains explicit or implicit mentions of the author's self beliefs using an annotated set of 2,000 human-annotated self beliefs, 100,000 LLM-labeled examples, and 10,000 surveyed self belief paragraphs. |
| Outcome: | The proposed model outperforms OpenAI’s state-of-the-art GPT-4o model in the AUC of 0.944 and annotates 2,000 human-annotated self beliefs, 100,000 LLM-labeled examples, and 10,000 surveyed self belief paragraphs. |