Walk the Talk? Measuring the Faithfulness of Large Language Model Explanations
Katie Matton Robert Ness John Guttag Emre Kiciman
Large language models (LLMs) are capable of generating *plausible* explanations of how they arrived at an answer to a question. However, these explanations can misrepresent the model's "reasoning" process, i.e., they can be *unfaithful*. This, in turn, can lead to over-trust and misuse. We introduce a new approach for measuring the faithfulness of LLM explanations. First, we provide a rigorous definition of faithfulness. Since LLM explanations mimic human explanations, they often reference high-level *concepts* in the input question that purportedly influenced the model. We define faithfulness in terms of the difference between the set of concepts that the LLM's *explanations imply* are influential and the set that *truly* are. Second, we present a novel method for estimating faithfulness that is based on: (1) using an auxiliary LLM to modify the values of concepts within model inputs to create realistic counterfactuals, and (2) using a hierarchical Bayesian model to quantify the causal effects of concepts at both the example- and dataset-level. Our experiments show that our method can be used to quantify and discover interpretable patterns of unfaithfulness. On a social bias task, we uncover cases where LLM explanations hide the influence of social bias. On a medical question answering task, we uncover cases where LLM explanations provide misleading claims about which pieces of evidence influenced the model's decisions.
large language models, faithful explanations, explainability, safety, counterfactual reasoning
We introduce a novel method for measuring the faithfulness of explanations given by LLMs.