1 citations · 1 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
Confidence Under the Hood: An Investigation into the Confidence-Probability Alignment in Large Language Models
Abhishek Kumar, Robert Morabito, Sanzhar Umbet +2
As the use of Large Language Models (LLMs) becomes more widespread, understanding their self-evaluation of confidence in generated responses becomes increasingly important as it is…
Debiasing should be Good and Bad: Measuring the Consistency of Debiasing Techniques in Language Models
Robert Morabito, Jad Kabbara, Ali Emami
Debiasing methods that seek to mitigate the tendency of Language Models (LMs) to occasionally output toxic or inappropriate text have recently gained traction. In this paper, we pr…