29 citations · 116 across the 12 of their papers we have counts for
12 papers
Customized large language models can outperform Community Notes in correcting misinformation
Xinyi Zhou, Ashish Sharma, Amy X. Zhang +1
Addressing misinformation in real-world settings is challenging: content is often multimodal; factuality judgments are nuanced and context-dependent; new events emerge rapidly acro…
IMBUE: Improving Interpersonal Effectiveness through Simulation and Just-in-time Feedback with Human-Language Model Interaction
Inna Wanyin Lin, Ashish Sharma, Christopher Michael Rytting +3
Navigating certain communication situations can be challenging due to individuals' lack of skills and the interference of strong emotions. However, effective learning opportunities…
A Computational Framework for Behavioral Assessment of LLM Therapists
Yu Ying Chiu, Ashish Sharma, Inna Wanyin Lin +1
The emergence of large language models (LLMs) like ChatGPT has increased interest in their use as therapists to address mental health challenges and the widespread lack of access t…
Facilitating Self-Guided Mental Health Interventions Through Human-Language Model Interaction: A Case Study of Cognitive Restructuring
Ashish Sharma, Kevin Rushton, Inna Wanyin Lin +2
Self-guided mental health interventions, such as "do-it-yourself" tools to learn and practice coping strategies, show great promise to improve access to mental health care. However…
Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction
Ashish Sharma, Kevin Rushton, Inna Wanyin Lin +5
A proven therapeutic technique to overcome negative thoughts is to replace them with a more hopeful "reframed thought." Although therapy can help people practice and learn this Cog…
Investigating Agency of LLMs in Human-AI Collaboration Tasks
Ashish Sharma, Sudha Rao, Chris Brockett +3
Agency, the capacity to proactively shape events, is central to how humans interact and collaborate. While LLMs are being developed to simulate human behavior and serve as human-li…
Gendered Mental Health Stigma in Masked Language Models
Inna Wanyin Lin, Lucille Njoo, Anjalie Field +4
Mental health stigma prevents many individuals from receiving the appropriate care, and social psychology studies have shown that mental health tends to be overlooked in men. In th…
Human-AI Collaboration Enables More Empathic Conversations in Text-based Peer-to-Peer Mental Health Support
Ashish Sharma, Inna W. Lin, Adam S. Miner +2
Advances in artificial intelligence (AI) are enabling systems that augment and collaborate with humans to perform simple, mechanistic tasks like scheduling meetings and grammar-che…
Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach
Ashish Sharma, Inna W. Lin, Adam S. Miner +2
Online peer-to-peer support platforms enable conversations between millions of people who seek and provide mental health support. If successful, web-based mental health conversatio…
A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support
Ashish Sharma, Adam S. Miner, David C. Atkins +1
Empathy is critical to successful mental health support. Empathy measurement has predominantly occurred in synchronous, face-to-face settings, and may not translate to asynchronous…
Engagement Patterns of Peer-to-Peer Interactions on Mental Health Platforms
Ashish Sharma, Monojit Choudhury, Tim Althoff +1
Mental illness is a global health problem, but access to mental healthcare resources remain poor worldwide. Online peer-to-peer support platforms attempt to alleviate this fundamen…
Improving Latent User Models in Online Social Media
Adit Krishnan, Ashish Sharma, Hari Sundaram
Modern social platforms are characterized by the presence of rich user-behavior data associated with the publication, sharing and consumption of textual content. Users interact wit…