  Large Language Models (LLMs) are deployed as powerful tools for several
natural language processing (NLP) applications. Recent works show that modern
LLMs can generate self-explanations (SEs), which elicit their intermediate
reasoning steps for explaining their behavior. Self-explanations have seen
widespread adoption owing to their conversational and plausible nature.
However, there is little to no understanding of their faithfulness. In this
work, we discuss the dichotomy between faithfulness and plausibility in SEs
generated by LLMs. We argue that while LLMs are adept at generating plausible
explanations -- seemingly logical and coherent to human users -- these
explanations do not necessarily align with the reasoning processes of the LLMs,
raising concerns about their faithfulness. 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. We assert that the faithfulness of explanations is critical in
LLMs employed for high-stakes decision-making. Moreover, we emphasize the need
for a systematic characterization of faithfulness-plausibility requirements of
different real-world applications and ensure explanations meet those needs.
While there are several approaches to improving plausibility, improving
faithfulness is an open challenge. We call upon the community to develop novel
methods to enhance the faithfulness of self explanations thereby enabling
transparent deployment of LLMs in diverse high-stakes settings.
