23 citations · 33 across the 12 of their papers we have counts for
13 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…
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…
MMCOMET: A Large-Scale Multimodal Commonsense Knowledge Graph for Visual Reasoning
Jinwoo Kim, Eileen Wang, Hiba Arnaout +13
Commonsense knowledge is widely used to support multimodal reasoning and generation, yet existing resources are predominantly text-only and lack grounding in visual context. This l…
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…
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…
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.…
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…
In-depth Research Impact Summarization through Fine-Grained Temporal Citation Analysis
Hiba Arnaout, Noy Sternlicht, Tom Hope +1
Understanding the impact of scientific publications is crucial for identifying breakthroughs and guiding future research. Traditional metrics based on citation counts often miss th…
Can large language models generate salient negative statements?
Hiba Arnaout, Simon Razniewski
We examine the ability of large language models (LLMs) to generate salient (interesting) negative statements about real-world entities; an emerging research topic of the last few y…
Completeness, Recall, and Negation in Open-World Knowledge Bases: A Survey
Simon Razniewski, Hiba Arnaout, Shrestha Ghosh +1
General-purpose knowledge bases (KBs) are a cornerstone of knowledge-centric AI. Many of them are constructed pragmatically from Web sources, and are thus far from complete. This p…
Wiki-based Communities of Interest: Demographics and Outliers
Hiba Arnaout, Simon Razniewski, Jeff Z. Pan
In this paper, we release data about demographic information and outliers of communities of interest. Identified from Wiki-based sources, mainly Wikidata, the data covers 7.5k comm…
UnCommonSense: Informative Negative Knowledge about Everyday Concepts
Hiba Arnaout, Simon Razniewski, Gerhard Weikum +1
Commonsense knowledge about everyday concepts is an important asset for AI applications, such as question answering and chatbots. Recently, we have seen an increasing interest in t…
Negative Statements Considered Useful
Hiba Arnaout, Simon Razniewski, Gerhard Weikum +1
Knowledge bases (KBs) about notable entities and their properties are an important asset in applications such as search, question answering and dialogue. All popular KBs capture vi…