How do you ensure that the LLM-generated explanations remain faithful to the underlying Random Forest model outputs and do not introduce hallucinated or misleading interpretations?
We ensure faithfulness by tightly constraining the LLM’s inputs and outputs to the Random Forest model’s signals rather than allowing free-form reasoning. The LLM receives only the classifier decision, confidence score, top-ranked feature importances, and structured anomaly context, which grounds the explanation in model-derived evidence. Prompting is task-constrained to summarization, root-cause inference, and remediation suggestion based explicitly on these inputs, preventing speculative interpretations. Additionally, low-temperature decoding is used to reduce variability, and the LLM does not generate explanations unless an anomaly is confirmed by the Random Forest, ensuring that all outputs remain consistent with the underlying model behavior.
