5 citations · 12 across the 7 of their papers we have counts for
6 papers · 1 filter
FinRT: Distilling Adaptive Red-Teaming Strategies into Reusable Adversarial Generators in Consumer Finance
Rikhiya Ghosh, Himanshu Kumar, Sriram Venkatapathy +3
In regulated industries like consumer finance, seemingly harmless user queries can exploit large language model vulnerabilities, triggering safety failures and pushing responses da…
CVE-LLM : Ontology-Assisted Automatic Vulnerability Evaluation Using Large Language Models
Rikhiya Ghosh, Hans-Martin von Stockhausen, Martin Schmitt +3
The National Vulnerability Database (NVD) publishes over a thousand new vulnerabilities monthly, with a projected 25 percent increase in 2024, highlighting the crucial need for rap…
CVE-LLM : Automatic vulnerability evaluation in medical device industry using large language models
Rikhiya Ghosh, Oladimeji Farri, Hans-Martin von Stockhausen +2
The healthcare industry is currently experiencing an unprecedented wave of cybersecurity attacks, impacting millions of individuals. With the discovery of thousands of vulnerabilit…
General-Purpose vs. Domain-Adapted Large Language Models for Extraction of Structured Data from Chest Radiology Reports
Ali H. Dhanaliwala, Rikhiya Ghosh, Sanjeev Kumar Karn +4
Radiologists produce unstructured data that can be valuable for clinical care when consumed by information systems. However, variability in style limits usage. Study compares syste…
shs-nlp at RadSum23: Domain-Adaptive Pre-training of Instruction-tuned LLMs for Radiology Report Impression Generation
Sanjeev Kumar Karn, Rikhiya Ghosh, Kusuma P +1
Instruction-tuned generative Large language models (LLMs) like ChatGPT and Bloomz possess excellent generalization abilities, but they face limitations in understanding radiology r…
RadLing: Towards Efficient Radiology Report Understanding
Rikhiya Ghosh, Sanjeev Kumar Karn, Manuela Daniela Danu +3
Most natural language tasks in the radiology domain use language models pre-trained on biomedical corpus. There are few pretrained language models trained specifically for radiolog…