4 citations · 18 across the 27 of their papers we have counts for
20 papers · 1 filter
MUDDLE: Measuring Understanding of Documents under Distractor and Length Effects
Jason Luo, Saibilila Abudukelimu, Judy Song +4
Document question-answering systems increasingly answer questions over collections of retrieved documents rather than one clean source, so robustness to distracting context matters…
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…
Error Reflection Prompting: Can Large Language Models Successfully Understand Errors?
Jason Li, Lauren Yraola, Kevin Zhu +1
Prompting methods for language models, such as Chain-of-thought (CoT), present intuitive step-by-step processes for problem solving. These methodologies aim to equip models with a…
Pruning for Performance: Efficient Idiom and Metaphor Classification in Low-Resource Konkani Using mBERT
Timothy Do, Pranav Saran, Harshita Poojary +4
In this paper, we address the persistent challenges that figurative language expressions pose for natural language processing (NLP) systems, particularly in low-resource languages…
MALIBU Benchmark: Multi-Agent LLM Implicit Bias Uncovered
Imran Mirza, Cole Huang, Ishwara Vasista +4
Multi-agent systems, which consist of multiple AI models interacting within a shared environment, are increasingly used for persona-based interactions. However, if not carefully de…