2 citations · 5 across the 13 of their papers we have counts for
13 papers
Token-Level Diagnosis of Sycophancy in LLMs with Attribution-Guided Steering
Hieu Nguyen, Mahammed Kamruzzaman, Anshuman Chhabra +1
Sycophancy refers to the tendency for large language models (LLMs) to match user beliefs at the cost of factual correctness, thereby undermining model reliability. Prior work on ev…
Demographic Prompting at Scale: When More Attributes Hurt LLM--Human Agreement
Mahammed Kamruzzaman, Shrabon Kumar Das, Gene Louis Kim
We investigate how annotator demographic attributes, supplied as prompt cues, shape the alignment between large language model (LLM) predictions and human annotations across five t…
Breaking the Benchmark: Revealing LLM Bias via Minimal Contextual Augmentation
Kaveh Eskandari Miandoab, Mahammed Kamruzzaman, Arshia Gharooni +3
Large Language Models have been shown to demonstrate stereotypical biases in their representations and behavior due to the discriminative nature of the data that they have been tra…
From Anger to Joy: How Nationality Personas Shape Emotion Attribution in Large Language Models
Mahammed Kamruzzaman, Abdullah Al Monsur, Gene Louis Kim +1
Emotions are a fundamental facet of human experience, varying across individuals, cultural contexts, and nationalities. Given the recent success of Large Language Models (LLMs) as…
The Impact of Disability Disclosure on Fairness and Bias in LLM-Driven Candidate Selection
Mahammed Kamruzzaman, Gene Louis Kim
As large language models (LLMs) become increasingly integrated into hiring processes, concerns about fairness have gained prominence. When applying for jobs, companies often reques…
"A Woman is More Culturally Knowledgeable than A Man?": The Effect of Personas on Cultural Norm Interpretation in LLMs
Mahammed Kamruzzaman, Hieu Nguyen, Nazmul Hassan +1
As the deployment of large language models (LLMs) expands, there is an increasing demand for personalized LLMs. One method to personalize and guide the outputs of these models is b…
BanStereoSet: A Dataset to Measure Stereotypical Social Biases in LLMs for Bangla
Mahammed Kamruzzaman, Abdullah Al Monsur, Shrabon Das +2
This study presents BanStereoSet, a dataset designed to evaluate stereotypical social biases in multilingual LLMs for the Bangla language. In an effort to extend the focus of bias…
"Global is Good, Local is Bad?": Understanding Brand Bias in LLMs
Mahammed Kamruzzaman, Hieu Minh Nguyen, Gene Louis Kim
Many recent studies have investigated social biases in LLMs but brand bias has received little attention. This research examines the biases exhibited by LLMs towards different bran…
Exploring Changes in Nation Perception with Nationality-Assigned Personas in LLMs
Mahammed Kamruzzaman, Gene Louis Kim
Persona assignment has become a common strategy for customizing LLM use to particular tasks and contexts. In this study, we explore how evaluation of different nations change when…
Prompting Techniques for Reducing Social Bias in LLMs through System 1 and System 2 Cognitive Processes
Mahammed Kamruzzaman, Gene Louis Kim
Dual process theory posits that human cognition arises via two systems. System 1, which is a quick, emotional, and intuitive process, which is subject to cognitive biases, and Syst…
BanMANI: A Dataset to Identify Manipulated Social Media News in Bangla
Mahammed Kamruzzaman, Md. Minul Islam Shovon, Gene Louis Kim
Initial work has been done to address fake news detection and misrepresentation of news in the Bengali language. However, no work in Bengali yet addresses the identification of spe…
Investigating Subtler Biases in LLMs: Ageism, Beauty, Institutional, and Nationality Bias in Generative Models
Mahammed Kamruzzaman, Md. Minul Islam Shovon, Gene Louis Kim
LLMs are increasingly powerful and widely used to assist users in a variety of tasks. This use risks the introduction of LLM biases to consequential decisions such as job hiring, h…
Efficient Sentiment Analysis: A Resource-Aware Evaluation of Feature Extraction Techniques, Ensembling, and Deep Learning Models
Mahammed Kamruzzaman, Gene Louis Kim
While reaching for NLP systems that maximize accuracy, other important metrics of system performance are often overlooked. Prior models are easily forgotten despite their possible…