4 citations · 18 across the 27 of their papers we have counts for
18 papers · 1 filter
Reasoning Relay: Evaluating Stability and Interchangeability of Large Language Models in Mathematical Reasoning
Leo Lu, Jonathan Zhang, Sean Chua +4
Chain-of-Thought (CoT) prompting has significantly advanced the reasoning capabilities of large language models (LLMs). While prior work focuses on improving model performance thro…
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…
SMAGDi: Socratic Multi Agent Interaction Graph Distillation for Efficient High Accuracy Reasoning
Aayush Aluru, Myra Malik, Samarth Patankar +4
Multi-agent systems (MAS) often achieve higher reasoning accuracy than single models, but their reliance on repeated debates across agents makes them computationally expensive. We…
SwiftSolve: A Self-Iterative, Complexity-Aware Multi-Agent Framework for Competitive Programming
Adhyayan Veer Singh, Aaron Shen, Brian Law +4
Correctness alone is insufficient: LLM-generated programs frequently satisfy unit tests while violating contest time or memory budgets. We present SwiftSolve, a complexity-aware mu…
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…