5 citations · 8 across the 19 of their papers we have counts for
9 papers · 1 filter
Testing the Utility of Using Large Language Models to Create Personalized Networks From Therapy Session Transcripts: A Proof of Concept Study
Clarissa W. Ong, Hiba Arnaout, Kate Sheehan +3
Recent advances in psychotherapy have focused on treatment personalization, such as by selecting treatment modules based on individual networks. However, estimating personalized ne…
MAGneT: Coordinated Multi-Agent Generation of Synthetic Multi-Turn Mental Health Counseling Sessions
Aishik Mandal, Tanmoy Chakraborty, Iryna Gurevych
The growing demand for scalable psychological counseling highlights the need for high-quality, privacy-compliant data, yet such data remains scarce. Here we introduce MAGneT, a nov…
A Comprehensive Review of Datasets for Clinical Mental Health AI Systems
Aishik Mandal, Prottay Kumar Adhikary, Hiba Arnaout +2
Mental health disorders are rising worldwide. However, the availability of trained clinicians has not scaled proportionally, leaving many people without adequate or timely support.…
Expert Preference-based Evaluation of Automated Related Work Generation
Furkan Şahinuç, Subhabrata Dutta, Iryna Gurevych
Expert domain writing, such as scientific writing, typically demands extensive domain knowledge. Although large language models (LLMs) show promising potential in this task, evalua…
Tailored Emotional LLM-Supporter: Enhancing Cultural Sensitivity
Chen Cecilia Liu, Hiba Arnaout, Nils Kovačić +2
Large language models (LLMs) show promise in offering emotional support and generating empathetic responses for individuals in distress, but their ability to deliver culturally sen…
From Surveys to Narratives: Rethinking Cultural Value Adaptation in LLMs
Muhammad Farid Adilazuarda, Chen Cecilia Liu, Iryna Gurevych +1
Adapting cultural values in Large Language Models (LLMs) presents significant challenges, particularly due to biases and limited training data. Prior work primarily aligns LLMs wit…
Cultural Learning-Based Culture Adaptation of Language Models
Chen Cecilia Liu, Anna Korhonen, Iryna Gurevych
Adapting large language models (LLMs) to diverse cultural values is a challenging task, as existing LLMs often reflect the values of specific groups by default, and potentially cau…
ObscuraCoder: Powering Efficient Code LM Pre-Training Via Obfuscation Grounding
Indraneil Paul, Haoyi Yang, Goran Glavaš +2
Language models (LMs) have become a staple of the code-writing toolbox. Their pre-training recipe has, however, remained stagnant over recent years, barring the occasional changes…
Enhancing Depression Detection via Question-wise Modality Fusion
Aishik Mandal, Dana Atzil-Slonim, Thamar Solorio +1
Depression is a highly prevalent and disabling condition that incurs substantial personal and societal costs. Current depression diagnosis involves determining the depression sever…