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Found 3 papers, 1 papers with code
Faithfulness vs. Plausibility: On the (Un)Reliability of Explanations from Large Language Models
We highlight that the current trend towards increasing the plausibility of explanations, primarily driven by the demand for user-friendly interfaces, may come at the cost of diminishing their faithfulness.
Quantifying Uncertainty in Natural Language Explanations of Large Language Models
In this work, we make one of the first attempts at quantifying the uncertainty in explanations of LLMs.
Word-Level Explanations for Analyzing Bias in Text-to-Image Models
We introduce a method for computing scores for each word in the prompt; these scores represent its influence on biases in the model's output.
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