2 citations · 4 across the 5 of their papers we have counts for
6 papers
SpectralCanary: Embarrassingly Simple Low-Frequency Traces for T2I Dataset-Use Auditing
Sy-Tuyen Ho, Anirudh Satheesh, Soham Chawathe +4
Auditing whether a text-to-image (T2I) model was fine-tuned on a particular image collection is difficult: a model can absorb collection-level visual characteristics without reprod…
Refining Input Guardrails: Enhancing LLM-as-a-Judge Efficiency Through Chain-of-Thought Fine-Tuning and Alignment
Melissa Kazemi Rad, Huy Nghiem, Andy Luo +3
Large Language Models (LLMs) have demonstrated powerful capabilities that render them valuable in different applications, including conversational AI products. It is paramount to e…
"You Gotta be a Doctor, Lin": An Investigation of Name-Based Bias of Large Language Models in Employment Recommendations
Huy Nghiem, John Prindle, Jieyu Zhao +1
Social science research has shown that candidates with names indicative of certain races or genders often face discrimination in employment practices. Similarly, Large Language Mod…
HateCOT: An Explanation-Enhanced Dataset for Generalizable Offensive Speech Detection via Large Language Models
Huy Nghiem, Hal Daumé
The widespread use of social media necessitates reliable and efficient detection of offensive content to mitigate harmful effects. Although sophisticated models perform well on ind…
"Define Your Terms" : Enhancing Efficient Offensive Speech Classification with Definition
Huy Nghiem, Umang Gupta, Fred Morstatter
The propagation of offensive content through social media channels has garnered attention of the research community. Multiple works have proposed various semantically related yet s…
"Stop Asian Hate!" : Refining Detection of Anti-Asian Hate Speech During the COVID-19 Pandemic
Huy Nghiem, Fred Morstatter
Content warning: This work displays examples of explicit and/or strongly offensive language. Fueled by a surge of anti-Asian xenophobia and prejudice during the COVID-19 pandemic,…