Researchers explore the possibility of using graph-based representations to improve the interpretability of Large Language Model (LLM) Natural Language Inference (NLI) systems. They introduce a pipeline that decomposes input text into atomic propositions and represents them as graphs, achieving 89.7% accuracy on the SNLI dataset. The study highlights the trade-off between accuracy and interpretability, with the graph-based approach trailing behind its text-based counterpart by 1.9 points. The authors also demonstrate that combining graph and text modalities can achieve higher accuracy.