6 citations · 12 across the 16 of their papers we have counts for
5 papers · 1 filter
Human-Readable Adversarial Prompts: An Investigation into LLM Vulnerabilities Using Situational Context
Nilanjana Das, Edward Raff, Aman Chadha +1
As the AI systems become deeply embedded in social media platforms, we've uncovered a concerning security vulnerability that goes beyond traditional adversarial attacks. It becomes…
Towards Robust Evaluation of Unlearning in LLMs via Data Transformations
Abhinav Joshi, Shaswati Saha, Divyaksh Shukla +4
Large Language Models (LLMs) have shown to be a great success in a wide range of applications ranging from regular NLP-based use cases to AI agents. LLMs have been trained on a vas…
Human-Interpretable Adversarial Prompt Attack on Large Language Models with Situational Context
Nilanjana Das, Edward Raff, Manas Gaur
Previous research on testing the vulnerabilities in Large Language Models (LLMs) using adversarial attacks has primarily focused on nonsensical prompt injections, which are easily…
IoT-Based Preventive Mental Health Using Knowledge Graphs and Standards for Better Well-Being
Amelie Gyrard, Seyedali Mohammadi, Manas Gaur +1
Sustainable Development Goals (SDGs) give the UN a road map for development with Agenda 2030 as a target. SDG3 "Good Health and Well-Being" ensures healthy lives and promotes well-…
WellDunn: On the Robustness and Explainability of Language Models and Large Language Models in Identifying Wellness Dimensions
Seyedali Mohammadi, Edward Raff, Jinendra Malekar +3
Language Models (LMs) are being proposed for mental health applications where the heightened risk of adverse outcomes means predictive performance may not be a sufficient litmus te…