  Instruction-tuned Large Language Models (LLMs) excel at many tasks and will
even explain their reasoning, so-called self-explanations. However, convincing
and wrong self-explanations can lead to unsupported confidence in LLMs, thus
increasing risk. Therefore, it's important to measure if self-explanations
truly reflect the model's behavior. Such a measure is called
interpretability-faithfulness and is challenging to perform since the ground
truth is inaccessible, and many LLMs only have an inference API. To address
this, we propose employing self-consistency checks to measure faithfulness. For
example, if an LLM says a set of words is important for making a prediction,
then it should not be able to make its prediction without these words. While
self-consistency checks are a common approach to faithfulness, they have not
previously been successfully applied to LLM self-explanations for
counterfactual, feature attribution, and redaction explanations. Our results
demonstrate that faithfulness is explanation, model, and task-dependent,
showing self-explanations should not be trusted in general. For example, with
sentiment classification, counterfactuals are more faithful for Llama2, feature
attribution for Mistral, and redaction for Falcon 40B.
