9 citations · 17 across the 13 of their papers we have counts for
5 papers · 1 filter
Assessing Bias in Metric Models for LLM Open-Ended Generation Bias Benchmarks
Nathaniel Demchak, Xin Guan, Zekun Wu +3
Open-generation bias benchmarks evaluate social biases in Large Language Models (LLMs) by analyzing their outputs. However, the classifiers used in analysis often have inherent bia…
Bias Amplification: Large Language Models as Increasingly Biased Media
Ze Wang, Zekun Wu, Jeremy Zhang +5
Model collapse, a phenomenon characterized by performance degradation due to iterative training on synthetic data, has been widely studied. However, its implications for bias ampli…
SAGED: A Holistic Bias-Benchmarking Pipeline for Language Models with Customisable Fairness Calibration
Xin Guan, Ze Wang, Nathaniel Demchak +5
The development of unbiased large language models is widely recognized as crucial, yet existing benchmarks fall short in detecting biases due to limited scope, contamination, and l…
HyPA-RAG: A Hybrid Parameter Adaptive Retrieval-Augmented Generation System for AI Legal and Policy Applications
Rishi Kalra, Zekun Wu, Ayesha Gulley +4
Large Language Models (LLMs) face limitations in AI legal and policy applications due to outdated knowledge, hallucinations, and poor reasoning in complex contexts. Retrieval-Augme…
JobFair: A Framework for Benchmarking Gender Hiring Bias in Large Language Models
Ze Wang, Zekun Wu, Xin Guan +6
The use of Large Language Models (LLMs) in hiring has led to legislative actions to protect vulnerable demographic groups. This paper presents a novel framework for benchmarking hi…