4 citations · 12 across the 11 of their papers we have counts for
11 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…
ChunkRAG: Novel LLM-Chunk Filtering Method for RAG Systems
Ishneet Sukhvinder Singh, Ritvik Aggarwal, Ibrahim Allahverdiyev +4
Retrieval-Augmented Generation (RAG) systems using large language models (LLMs) often generate inaccurate responses due to the retrieval of irrelevant or loosely related informatio…
Semantic Self-Consistency: Enhancing Language Model Reasoning via Semantic Weighting
Tim Knappe, Ryan Li, Ayush Chauhan +3
While large language models (LLMs) have rapidly improved their performance on a broad number of tasks, they still often fall short on reasoning tasks. As LLMs become more integrate…
From Bias to Balance: Detecting Facial Expression Recognition Biases in Large Multimodal Foundation Models
Kaylee Chhua, Zhoujinyi Wen, Vedant Hathalia +2
This study addresses the racial biases in facial expression recognition (FER) systems within Large Multimodal Foundation Models (LMFMs). Despite advances in deep learning and the a…
Chain-of-Thought Augmentation with Logit Contrast for Enhanced Reasoning in Language Models
Jay Shim, Grant Kruttschnitt, Alyssa Ma +5
Rapidly increasing model scales coupled with steering methods such as chain-of-thought prompting have led to drastic improvements in language model reasoning. At the same time, mod…
Question-Analysis Prompting Improves LLM Performance in Reasoning Tasks
Dharunish Yugeswardeenoo, Kevin Zhu, Sean O'Brien
Although LLMs have the potential to transform many fields, they still underperform humans in reasoning tasks. Existing methods induce the model to produce step-by-step calculations…
Improving LLM Abilities in Idiomatic Translation
Sundesh Donthi, Maximilian Spencer, Om Patel +4
For large language models (LLMs) like NLLB and GPT, translating idioms remains a challenge. Our goal is to enhance translation fidelity by improving LLM processing of idiomatic lan…