3 citations · 3 across the 5 of their papers we have counts for
5 papers
CBMAS: Cognitive Behavioral Modeling via Activation Steering
Ahmed H. Ismail, Anthony Kuang, Ayo Akinkugbe +2
Large language models (LLMs) often encode cognitive behaviors unpredictably across prompts, layers, and contexts, making them difficult to diagnose and control. We present CBMAS, a…
Direct Confidence Alignment: Aligning Verbalized Confidence with Internal Confidence In Large Language Models
Glenn Zhang, Treasure Mayowa, Jason Fan +4
Producing trustworthy and reliable Large Language Models (LLMs) has become increasingly important as their usage becomes more widespread. Calibration seeks to achieve this by impro…
Adaptive Linguistic Prompting (ALP) Enhances Phishing Webpage Detection in Multimodal Large Language Models
Atharva Bhargude, Ishan Gonehal, Dave Yoon +4
Phishing attacks represent a significant cybersecurity threat, necessitating adaptive detection techniques. This study explores few-shot Adaptive Linguistic Prompting (ALP) in dete…
EnDive: A Cross-Dialect Benchmark for Fairness and Performance in Large Language Models
Abhay Gupta, Jacob Cheung, Philip Meng +4
The diversity of human language, shaped by social, cultural, and regional influences, presents significant challenges for natural language processing (NLP) systems. Existing benchm…
CLEAR: Contrasting Textual Feedback with Experts and Amateurs for Reasoning
Andrew Rufail, Daniel Kim, Sean O'Brien +1
We introduce CLEAR (Contrasting Textual Feedback with Experts and Amateurs for Reasoning), a novel approach to language model reasoning that leverages the strengths of a larger (ex…