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cs.AI2023
Evaluating the Utility of Model Explanations for Model Development
Shawn Im, Jacob Andreas, Yilun Zhou
One of the motivations for explainable AI is to allow humans to make better and more informed decisions regarding the use and deployment of AI models. But careful evaluations are n…
cs.CL2023
Cognitive Dissonance: Why Do Language Model Outputs Disagree with Internal Representations of Truthfulness?
Kevin Liu, Stephen Casper, Dylan Hadfield-Menell +1
Neural language models (LMs) can be used to evaluate the truth of factual statements in two ways: they can be either queried for statement probabilities, or probed for internal rep…