9 citations · 19 across the 9 of their papers we have counts for
4 papers · 1 filter
Agree to Disagree? A Meta-Evaluation of LLM Misgendering
Arjun Subramonian, Vagrant Gautam, Preethi Seshadri +3
Numerous methods have been proposed to measure LLM misgendering, including probability-based evaluations (e.g., automatically with templatic sentences) and generation-based evaluat…
Small Changes, Large Consequences: Analyzing the Allocational Fairness of LLMs in Hiring Contexts
Preethi Seshadri, Hongyu Chen, Sameer Singh +1
Large language models (LLMs) are increasingly being deployed in high-stakes applications like hiring, yet their potential for unfair decision-making remains understudied in generat…
Are Models Biased on Text without Gender-related Language?
Catarina G Belém, Preethi Seshadri, Yasaman Razeghi +1
Gender bias research has been pivotal in revealing undesirable behaviors in large language models, exposing serious gender stereotypes associated with occupations, and emotions. A…
Quantifying Social Biases Using Templates is Unreliable
Preethi Seshadri, Pouya Pezeshkpour, Sameer Singh
Recently, there has been an increase in efforts to understand how large language models (LLMs) propagate and amplify social biases. Several works have utilized templates for fairne…