Self-Interpretability: LLMs Can Describe Complex Internal Processes that Drive Their Decisions

The study on self-interpretability of large language models (LLMs) reveals that models such as GPT-4o and GPT-4o-mini can articulate the quantitative aspects of their internal processes during decision-making. This advancement is crucial given the historically limited understanding of LLM responses. By fine-tuning these models to navigate complex contexts—like selecting between condos or loans—researchers found that LLMs could accurately report their learned preferences, enhancing their ability to explain decisions. This capability not only sheds light on the inner workings of LLMs but also suggests that further training can refine these interpretative skills, leading to improved performance in real-world applications. As AI continues to evolve, understanding its decision-making processes becomes increasingly vital for ensuring transparency and reliability.