Large language models (LLMs) are impressive, but can they explain *why* they make certain decisions? A new research paper digs into the reliability of LLM self-explanations, exploring whether these explanations offer true insights into their reasoning process, or are just well-crafted illusions. The researchers evaluated different types of LLM-generated explanations, ranging from simple extraction of important phrases to more complex counterfactual examples where a small change in the input text should flip the model's output. The results are interesting, showing that these explanations can sometimes align well with human intuition, especially on objective tasks like identifying food hazards. However, when it comes to subjective tasks like deciphering the sentiment in a movie review, the explanations often correlate poorly with human judgment and analytic explainability methods. This suggests LLMs aren't actually introspecting but learning to generate explanations that resonate with human expectations based on statistical correlations in training data. The work also delves into the limitations of current analytic techniques for interpreting LLM behavior, offering counterfactuals as a promising alternative, along with highlighting the significant impact prompt engineering has on explanation quality. While LLMs have shown promising results in tasks like answering factual questions, the mystery surrounding their inner workings remains, raising concerns about their trustworthiness and accountability.