Decoding LLM Reasoning: Causal Bayes Nets for Enhanced Interpretability

This research explores a novel method for interpreting the reasoning processes of Large Language Models (LLMs) using Noisy-OR causal Bayes nets. The approach offers potential for improving the understanding and trustworthiness of LLM outputs by dissecting their causal dependencies.