3 papers
cs.CL2026
Rating the Pitch, Not the Product: User Evaluations of LLMs Reflect Expectations More Than Performance
Robert Morabito, Tyler McDonald, Charitra Viswanath +4
Imagine two users interact with the same LLM. One has been told it is the cutting-edge flagship model; the other, an older, weaker model. They walk away with markedly different rat…
cs.CL2025
Fine-Tuned LLMs are "Time Capsules" for Tracking Societal Bias Through Books
Sangmitra Madhusudan, Robert Morabito, Skye Reid +2
Books, while often rich in cultural insights, can also mirror societal biases of their eras - biases that Large Language Models (LLMs) may learn and perpetuate during training. We…
cs.CL2025
STOP! Benchmarking Large Language Models with Sensitivity Testing on Offensive Progressions
Robert Morabito, Sangmitra Madhusudan, Tyler McDonald +1
Mitigating explicit and implicit biases in Large Language Models (LLMs) has become a critical focus in the field of natural language processing. However, many current methodologies…