4 citations · 5 across the 10 of their papers we have counts for
10 papers
The Mirage of LLM Guardrails: A Case Study in AI-Assisted Medical Note Manipulation
Davis Yadav, Amulya Yadav
The rapid deployment of large language models (LLMs) in healthcare settings makes the reliability of their built-in guardrails against malicious queries a question of urgent practi…
Evaluating Large Language Models on Rare Disease Diagnosis: A Case Study using House M.D
Arsh Gupta, Ajay Narayanan Sridhar, Bonam Mingole +1
Large language models (LLMs) have demonstrated capabilities across diverse domains, yet their performance on rare disease diagnosis from narrative medical cases remains underexplor…
Designing User-Centric Metrics for Evaluation of Counterfactual Explanations
Firdaus Ahmed Choudhury, Ethan Leicht, Jude Ethan Bislig +2
Counterfactual Explanations (CFEs) have grown in popularity as a means of offering actionable guidance by identifying the minimum changes in feature values required to flip an ML m…
Dr. GPT Will See You Now, but Should It? Exploring the Benefits and Harms of Large Language Models in Medical Diagnosis using Crowdsourced Clinical Cases
Bonam Mingole, Aditya Majumdar, Firdaus Ahmed Choudhury +3
The proliferation of Large Language Models (LLMs) in high-stakes applications such as medical (self-)diagnosis and preliminary triage raises significant ethical and practical conce…
The Hardness of Achieving Impact in AI for Social Impact Research: A Ground-Level View of Challenges & Opportunities
Aditya Majumdar, Wenbo Zhang, Kashvi Prawal +1
AI for Social Impact (AI4SI) is an emergent field harnessing interdisciplinarities between the fields of artificial intelligence (AI), machine learning (ML), and the social science…
CHAI for LLMs: Improving Code-Mixed Translation in Large Language Models through Reinforcement Learning with AI Feedback
Wenbo Zhang, Aditya Majumdar, Asif Ekbal +1
Large language models (LLMs) show strong performance across many tasks but remain weak at understanding code-mixed (CM) language. Despite this limitation, improving LLMs for CM tas…
Hey GPT, Can You be More Racist? Analysis from Crowdsourced Attempts to Elicit Biased Content from Generative AI
Hangzhi Guo, Pranav Narayanan Venkit, Eunchae Jang +7
The widespread adoption of large language models (LLMs) and generative AI (GenAI) tools across diverse applications has amplified the importance of addressing societal biases inher…
Have LLMs Reopened the Pandora's Box of AI-Generated Fake News?
Xinyu Wang, Wenbo Zhang, Sai Koneru +5
With the rise of AI-generated content spewed at scale from large language models (LLMs), genuine concerns about the spread of fake news have intensified. The perceived ability of L…
Watermarking Counterfactual Explanations
Hangzhi Guo, Firdaus Ahmed Choudhury, Tinghua Chen +1
Counterfactual (CF) explanations for ML model predictions provide actionable recourse recommendations to individuals adversely impacted by predicted outcomes. However, despite bein…
A Taxonomy of Rater Disagreements: Surveying Challenges & Opportunities from the Perspective of Annotating Online Toxicity
Wenbo Zhang, Hangzhi Guo, Ian D Kivlichan +3
Toxicity is an increasingly common and severe issue in online spaces. Consequently, a rich line of machine learning research over the past decade has focused on computationally det…