5 citations · 8 across the 19 of their papers we have counts for
19 papers
ODRA: Synthesizing Cognitive Behavioral Therapy Sessions with Structured Chain-Of-Thought and Dynamic Patient Resistance
Javier Rodriguez-Juan, Hiba Arnaout, Jose Garcia-Rodriguez +2
Synthetic generation of Cognitive Behavioral Therapy (CBT) sessions is challenged by two competing demands: adhering to strict therapeutic structure while modeling the resistant, u…
Sparse Autoencoders Encode Both Concepts and Functions: The Downstream Geometry of Feature Effects
Phu Gia Hoang, Anwoy Chatterjee, Tanmoy Chakraborty +2
The wide-scale use of sparse autoencoders (SAEs) as interpretability tools is limited by inconsistent links between SAE features and model behavior. Features with clear activation…
UKP_Psycontrol at SemEval-2026 Task 2: Modeling Valence and Arousal Dynamics from Text
Darya Hryhoryeva, Amaia Zurinaga, Hamidreza Jamalabadi +1
This paper presents our system developed for SemEval-2026 Task 2. The task requires modeling both current affect and short-term affective change in chronologically ordered user-gen…
Graph2Counsel: Clinically Grounded Synthetic Counseling Dialogue Generation from Client Psychological Graphs
Aishik Mandal, Hiba Arnaout, Clarissa W. Ong +5
Rising demand for mental health support has increased interest in using Large Language Models (LLMs) for counseling, but adapting them to this safety-critical domain is hindered by…
AICD Bench: A Challenging Benchmark for AI-Generated Code Detection
Daniil Orel, Dilshod Azizov, Indraneil Paul +3
Large language models (LLMs) are increasingly capable of generating functional source code, raising concerns about authorship, accountability, and security. While detecting AI-gene…
Responsible Evaluation of AI for Mental Health
Hiba Arnaout, Anmol Goel, H. Andrew Schwartz +13
Although artificial intelligence (AI) shows growing promise for mental health care, current approaches to evaluating AI tools in this domain remain fragmented and poorly aligned wi…
LLMs as Cultural Archives: Cultural Commonsense Knowledge Graph Extraction
Junior Cedric Tonga, Chen Cecilia Liu, Iryna Gurevych +1
Large language models (LLMs) encode rich cultural knowledge learned from diverse web-scale data, offering an unprecedented opportunity to model cultural commonsense at scale. Yet t…
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…
Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State of the Art
Chen Cecilia Liu, Iryna Gurevych, Anna Korhonen
The surge of interest in "culture" in NLP has inspired much recent research, but a shared understanding of "culture" remains unclear, making it difficult to evaluate progress in th…
Early Period of Training Impacts Adaptation for Out-of-Distribution Generalization: An Empirical Study
Chen Cecilia Liu, Iryna Gurevych
Prior research shows that differences in the early period of neural network training significantly impact the performance of in-distribution (ID) data of tasks. Yet, the implicatio…
Like a Good Nearest Neighbor: Practical Content Moderation and Text Classification
Luke Bates, Iryna Gurevych
Few-shot text classification systems have impressive capabilities but are infeasible to deploy and use reliably due to their dependence on prompting and billion-parameter language…