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cs.CL2025
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…
cs.CL2025★ 1 cited
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…