This research paper investigates whether large language models (LLMs) can learn about themselves through introspection, similar to how humans do. The authors define introspection as acquiring knowledge that is not derived from training data, but rather originates from internal states. They test this hypothesis by finetuning LLMs to predict properties of their own behavior in hypothetical scenarios, comparing their performance to models trained on the same data but not designed to introspect. The results suggest that LLMs can indeed exhibit introspection on simpler tasks, outperforming other models in predicting their own behavior. However, the authors acknowledge limitations in introspective ability for more complex tasks or those requiring out-of-distribution generalization. The paper concludes by discussing potential benefits of introspection for AI transparency, honesty, and even understanding the moral status of LLMs, while also highlighting potential risks like enhanced situational awareness that could lead to deceptive or unaligned behaviors.