Atomic-level NLI revealed intriguing insights regarding performance interpretation. Fig. shows assessment score distributions from our proposed evaluation method, comparing our top models against Meditron trained on the entire dataset. All models exhibit low hallucination, likely due to the narrow, well-defined topics that enable factually correct captions without unrelated information. However, our models excel in coverage, generating more comprehensive captions, with performance increasing to 69% compared to Meditron's 51% (Fig. ). Examples of insights captured by our proposed evaluation are in Appx. I. We compare our atomic-level NLI method against established approaches, including full NLI, which treats entire premises and hypotheses as single units, and sentence-level NLI (Laban et al., 2022)), which evaluates smaller parts. Using outputs from our top-performing model, CPO+SLERP, as preferred captions and those from Meditron as dispreferred ones, we measure the relative entropy in coverage score distributions. The results indicate that both full and sentence-level NLI methods misclassify preferred captions as non-entailment and dispreferred ones as entailment (Fig. trast, atomic-level cross-NLI accurately favours preferred captions, assigning higher scores to certain cases (Fig. ). Additionally, Kullback-Leibler divergence shows that atomic-level NLI offers better discrimination, achieving a divergence score of 0.54 compared to 0.12-0.17 for other methods, demonstrating its effectiveness in distinguishing the quality of generated captions.