  Large Language Models (LLMs) have become proficient in addressing complex
tasks by leveraging their extensive internal knowledge and reasoning
capabilities. However, the black-box nature of these models complicates the
task of explaining their decision-making processes. While recent advancements
demonstrate the potential of leveraging LLMs to self-explain their predictions
through natural language (NL) explanations, their explanations may not
accurately reflect the LLMs' decision-making process due to a lack of fidelity
optimization on the derived explanations. Measuring the fidelity of NL
explanations is a challenging issue, as it is difficult to manipulate the input
context to mask the semantics of these explanations. To this end, we introduce
FaithLM to explain the decision of LLMs with NL explanations. Specifically,
FaithLM designs a method for evaluating the fidelity of NL explanations by
incorporating the contrary explanations to the query process. Moreover, FaithLM
conducts an iterative process to improve the fidelity of derived explanations.
Experiment results on three datasets from multiple domains demonstrate that
FaithLM can significantly improve the fidelity of derived explanations, which
also provides a better alignment with the ground-truth explanations.
