Instruction-tuned large language models (LLMs) excel at many tasks, and will even provide explanations for their behavior. Since these models are directly accessible to the public, there is a risk that convincing and wrong explanations can lead to unsupported confidence in LLMs. Therefore, interpretability-faithfulness of self-explanations is an important consideration for AI Safety. Assessing the interpretability-faithfulness of these explanations, termed self-explanations, is challenging as the models are too complex for humans to annotate what is a correct explanation. To address this, we propose employing self-consistency checks as a measure of 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 the same prediction without these words. While self-consistency checks are a common approach to faithfulness, they have not previously been applied to LLM's self-explanations. We apply self-consistency checks to three types of self-explanations: counterfactuals, importance measures, and redactions. Our work demonstrate that faithfulness is both task and model dependent, e.g., for sentiment classification, counterfactual explanations are more faithful for Llama2, importance measures for Mistral, and redaction for Falcon 40B. Finally, our findings are robust to prompt-variations.

翻译：指令微调的大型语言模型（LLMs）在许多任务上表现出色，甚至能为其行为提供解释。由于这些模型可直接供公众使用，存在一种风险：令人信服但错误的解释可能导致对LLMs产生无根据的信任。因此，自我解释的可解释忠实性对于人工智能安全而言是一个重要考量。评估这些解释（称为自我解释）的可解释忠实性颇具挑战性，因为模型过于复杂，人类难以标注何为正确的解释。为解决这一问题，我们提出采用自洽性检查作为忠实性度量。例如，如果一个大语言模型称一组词对做出预测很重要，那么若没有这些词，它应无法做出相同的预测。尽管自洽性检查是评估忠实性的常用方法，但此前尚未应用于LLM的自我解释。我们将自洽性检查应用于三类自我解释：反事实解释、重要性度量和遮盖解释。我们的工作表明，忠实性既依赖于任务也依赖于模型，例如，在情感分类任务中，反事实解释对Llama2更忠实，重要性度量对Mistral更忠实，而遮盖解释对Falcon 40B更忠实。最后，我们的发现对提示变化具有稳健性。