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.

• •Applies causal Bayes nets to enhance the interpretability of LLM reasoning.
• •Utilizes Noisy-OR models to represent causal relationships within LLMs.
• •Aims to improve trust and understanding of LLM outputs through detailed analysis.