Large language models (LLMs) are impressive, but can they explain how they arrive at their conclusions? This question lies at the heart of building truly trustworthy and interpretable AI. A new research paper, "Evaluating Human Alignment and Model Faithfulness of LLM Rationale," tackles this challenge by examining how well LLMs can justify their decisions through extracted snippets of text called "rationales." The researchers explored two main approaches to generating these rationales: the popular prompting-based methods, where we ask the LLM to explain itself, and more technical attribution-based methods that analyze the model's internal workings, like attention mechanisms. The study tested these approaches on three different text classification datasets with varying levels of model performance. Surprisingly, simply prompting LLMs to explain themselves wasn't always reliable. These explanations frequently didn't match human understanding of the task, and were less faithful to the model's actual decision process than the attribution-based methods. This suggests that LLMs might be generating plausible-sounding explanations without truly reflecting their internal reasoning. Interestingly, fine-tuning the models on these datasets did improve the alignment and faithfulness of the attribution-based methods. However, it revealed a key challenge in evaluating faithfulness: poorly performing models tend to exhibit a bias toward specific predictions. This makes it hard to determine if an explanation is truly influential, as the model might stick with its initial guess regardless of which input words are masked. This research highlights the critical importance of thoroughly testing and analyzing how LLMs generate rationales. Building AI that can justify its decisions in a human-understandable way is essential for deploying LLMs in real-world applications, especially where trust and transparency are paramount. As LLMs continue to evolve, developing robust methods for evaluating their self-explanations will remain a crucial area of focus.