2 citations · 2 across the 1 of their papers we have counts for
3 papers
Distinguishing Scams and Fraud with Ensemble Learning
Isha Chadalavada, Tianhui Huang, Jessica Staddon
Users increasingly query LLM-enabled web chatbots for help with scam defense. The Consumer Financial Protection Bureau's complaints database is a rich data source for evaluating LL…
Learned, Lagged, LLM-splained: LLM Responses to End User Security Questions
Vijay Prakash, Kevin Lee, Arkaprabha Bhattacharya +2
Answering end user security questions is challenging. While large language models (LLMs) like GPT, LLAMA, and Gemini are far from error-free, they have shown promise in answering a…
Can LLMs be Scammed? A Baseline Measurement Study
Udari Madhushani Sehwag, Kelly Patel, Francesca Mosca +2
Despite the importance of developing generative AI models that can effectively resist scams, current literature lacks a structured framework for evaluating their vulnerability to s…