15 citations · 21 across the 15 of their papers we have counts for
18 papers
Debias It Yourself: Teaching LLMs Cognitive Bias Mitigation Interventions
Chahat Raj, Sina Mansouri, Aylin Caliskan +2
Bias has long been studied in social psychology and cognitive science, where decades of research have produced a body of validated interventions that reduce stereotypical thinking…
SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD
Dongfang Li, Xiaodong Luo, Ruoyu Sun +62
Full-parameter post-training of trillion-parameter-scale MoE models introduces substantial system-level challenges for large-scale distributed training, including severe memory pre…
Knowing Bias, Doing Better: Mitigating Social Bias in LLMs via Know-Bias Neuron Enhancement
Jinhao Pan, Chahat Raj, Anjishnu Mukherjee +4
Large language models (LLMs) exhibit social biases that reinforce harmful stereotypes, limiting their safe deployment. Most existing debiasing methods adopt a suppressive paradigm…
MAPLE: Metadata Conditioned LLM Pretraining for Locale-Aware Question Answering
Anjishnu Mukherjee, Ziwei Zhu, Antonios Anastasopoulos
Large language models can memorize competing locale-specific facts yet fail to select among them when the locale changes, defaulting instead to a single globally dominant answer. W…
ORGEval: Graph-Theoretic Evaluation of LLMs in Optimization Modeling
Zhuohan Wang, Ziwei Zhu, Ziniu Li +8
Formulating optimization problems for industrial applications demands significant manual effort and domain expertise. While Large Language Models (LLMs) show promise in automating…
ChronoPlay: A Framework for Modeling Dual Dynamics and Authenticity in Game RAG Benchmarks
Liyang He, Yuren Zhang, Ziwei Zhu +2
Retrieval Augmented Generation (RAG) systems are increasingly vital in dynamic domains like online gaming, yet the lack of a dedicated benchmark has impeded standardized evaluation…
Bias Association Discovery Framework for Open-Ended LLM Generations
Jinhao Pan, Chahat Raj, Ziwei Zhu
Social biases embedded in Large Language Models (LLMs) raise critical concerns, resulting in representational harms -- unfair or distorted portrayals of demographic groups -- that…
Spurious Correlations and Beyond: Understanding and Mitigating Shortcut Learning in SDOH Extraction with Large Language Models
Fardin Ahsan Sakib, Ziwei Zhu, Karen Trister Grace +2
Social determinants of health (SDOH) extraction from clinical text is critical for downstream healthcare analytics. Although large language models (LLMs) have shown promise, they m…
Talent or Luck? Evaluating Attribution Bias in Large Language Models
Chahat Raj, Mahika Banerjee, Jinhao Pan +3
When a student fails an exam, do we tend to blame their effort or the test's difficulty? Attribution, defined as how reasons are assigned to event outcomes, shapes perceptions, rei…
VIGNETTE: Socially Grounded Bias Evaluation for Vision-Language Models
Chahat Raj, Bowen Wei, Aylin Caliskan +2
While bias in large language models (LLMs) is well-studied, similar concerns in vision-language models (VLMs) have received comparatively less attention. Existing VLM bias studies…
Purdah and Patriarchy: Evaluating and Mitigating South Asian Biases in Open-Ended Multilingual LLM Generations
Mamnuya Rinki, Chahat Raj, Anjishnu Mukherjee +1
Evaluations of Large Language Models (LLMs) often overlook intersectional and culturally specific biases, particularly in underrepresented multilingual regions like South Asia. Thi…
ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System
Rahul Pandey, Ziwei Zhu, Hemant Purohit
Effective labeled data collection plays a critical role in developing and fine-tuning robust streaming analytics systems. However, continuously labeling documents to filter relevan…
Assessing Large Language Models for Online Extremism Research: Identification, Explanation, and New Knowledge
Beidi Dong, Jin R. Lee, Ziwei Zhu +1
The United States has experienced a significant increase in violent extremism, prompting the need for automated tools to detect and limit the spread of extremist ideology online. T…
Breaking Bias, Building Bridges: Evaluation and Mitigation of Social Biases in LLMs via Contact Hypothesis
Chahat Raj, Anjishnu Mukherjee, Aylin Caliskan +2
Large Language Models (LLMs) perpetuate social biases, reflecting prejudices in their training data and reinforcing societal stereotypes and inequalities. Our work explores the pot…
Crossroads of Continents: Automated Artifact Extraction for Cultural Adaptation with Large Multimodal Models
Anjishnu Mukherjee, Ziwei Zhu, Antonios Anastasopoulos
We present a comprehensive three-phase study to examine (1) the cultural understanding of Large Multimodal Models (LMMs) by introducing DalleStreet, a large-scale dataset generated…
BiasDora: Exploring Hidden Biased Associations in Vision-Language Models
Chahat Raj, Anjishnu Mukherjee, Aylin Caliskan +2
Existing works examining Vision-Language Models (VLMs) for social biases predominantly focus on a limited set of documented bias associations, such as gender:profession or race:cri…
Global Voices, Local Biases: Socio-Cultural Prejudices across Languages
Anjishnu Mukherjee, Chahat Raj, Ziwei Zhu +1
Human biases are ubiquitous but not uniform: disparities exist across linguistic, cultural, and societal borders. As large amounts of recent literature suggest, language models (LM…
Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearning
Zheyuan Liu, Guangyao Dou, Yijun Tian +3
Machine Unlearning (MU) algorithms have become increasingly critical due to the imperative adherence to data privacy regulations. The primary objective of MU is to erase the influe…