Search Results for author:
Found 7 papers, 2 papers with code
Advancing NLP Security by Leveraging LLMs as Adversarial Engines
This position paper proposes a novel approach to advancing NLP security by leveraging Large Language Models (LLMs) as engines for generating diverse adversarial attacks.
Hiding-in-Plain-Sight (HiPS) Attack on CLIP for Targetted Object Removal from Images
Machine learning models are known to be vulnerable to adversarial attacks, but traditional attacks have mostly focused on single-modalities.
Leveraging Large Language Models to Extract Information on Substance Use Disorder Severity from Clinical Notes: A Zero-shot Learning Approach
SUD identification and treatment depend on a variety of factors such as severity, co-determinants (e. g., withdrawal symptoms), and social determinants of health.
Dynamic Q&A of Clinical Documents with Large Language Models
Electronic health records (EHRs) house crucial patient data in clinical notes.
Question-Answering System Extracts Information on Injection Drug Use from Clinical Notes
We also demonstrate the QA model's ability to extract IDU-related information on temporally out-of-distribution data.
BioADAPT-MRC: Adversarial Learning-based Domain Adaptation Improves Biomedical Machine Reading Comprehension Task
We present an adversarial learning-based domain adaptation framework for the biomedical machine reading comprehension task (BioADAPT-MRC), a neural network-based method to address the discrepancies in the marginal distributions between the general and biomedical domain datasets.
The Sensitivity of Word Embeddings-based Author Detection Models to Semantic-preserving Adversarial Perturbations
To this end, and using those established techniques, we first developed an experimental frame-work for author detection and input perturbations.
