46 citations · 52 across the 12 of their papers we have counts for
12 papers
What Users Cannot See: Evaluating LLM Emotional Support Beyond User Preference
Vivienne Bihe Chi, Adithya V Ganesan, Ryan L Boyd +2
People increasingly turn to LLMs for emotional support, yet common evaluations reward responses that feel helpful and may miss consequential response behaviors. We introduce a theo…
A Systematic Evaluation of Large Language Models for PTSD Severity Estimation: The Role of Contextual Knowledge and Modeling Strategies
Panagiotis Kaliosis, Adithya V Ganesan, Oscar N. E. Kjell +8
Large language models (LLMs) are increasingly being used in a zero-shot (generative) fashion to assess mental health conditions, yet we have limited knowledge on what factors affec…
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…
Idiosyncratic Versus Normative Modeling of Atypical Speech Recognition: Dysarthric Case Studies
Vishnu Raja, Adithya V Ganesan, Anand Syamkumar +2
State-of-the-art automatic speech recognition (ASR) models like Whisper, perform poorly on atypical speech, such as that produced by individuals with dysarthria. Past works for aty…
Inferring Effects of Major Events through Discontinuity Forecasting of Population Anxiety
Siddharth Mangalik, Ojas Deshpande, Adithya V. Ganesan +2
Estimating community-specific mental health effects of local events is vital for public health policy. While forecasting mental health scores alone offers limited insights into the…
WhiSPA: Semantically and Psychologically Aligned Whisper with Self-Supervised Contrastive and Student-Teacher Learning
Rajath Rao, Adithya Ganesan, Oscar Kjell +10
Current speech encoding pipelines often rely on an additional text-based LM to get robust representations of human communication, even though SotA speech-to-text models often have…
PsychAdapter: Adapting LLM Transformers to Reflect Traits, Personality and Mental Health
Huy Vu, Huy Anh Nguyen, Adithya V Ganesan +8
Artificial intelligence-based language generators are now a part of most people's lives. However, by default, they tend to generate "average" language without reflecting the ways i…
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…
SOCIALITE-LLAMA: An Instruction-Tuned Model for Social Scientific Tasks
Gourab Dey, Adithya V Ganesan, Yash Kumar Lal +6
Social science NLP tasks, such as emotion or humor detection, are required to capture the semantics along with the implicit pragmatics from text, often with limited amounts of trai…
Systematic Evaluation of GPT-3 for Zero-Shot Personality Estimation
Adithya V Ganesan, Yash Kumar Lal, August Håkan Nilsson +1
Very large language models (LLMs) perform extremely well on a spectrum of NLP tasks in a zero-shot setting. However, little is known about their performance on human-level NLP prob…
Robust language-based mental health assessments in time and space through social media
Siddharth Mangalik, Johannes C. Eichstaedt, Salvatore Giorgi +8
Compared to physical health, population mental health measurement in the U.S. is very coarse-grained. Currently, in the largest population surveys, such as those carried out by the…
Empirical Evaluation of Pre-trained Transformers for Human-Level NLP: The Role of Sample Size and Dimensionality
Adithya V Ganesan, Matthew Matero, Aravind Reddy Ravula +2
In human-level NLP tasks, such as predicting mental health, personality, or demographics, the number of observations is often smaller than the standard 768+ hidden state sizes of e…