We also propose an atomic-level NLI evaluation method that successfully handles multiple content units, offering valuable insights into performance interpretability for caption generation, where widely adopted NLI methods consistently misalign with assessment criteria. However, decomposing text into atomic units can be challenging for other tasks involving complex or lengthy text. While this method captures nuanced content, there is a risk of over-fragmentation, which may lead to a loss of context or coherence in evaluation. Additionally, the effectiveness of this approach relies heavily on the LLM for decomposition and the NLI model for entailment and contradiction assessment. If either model struggles with domain-specific content (e.g., highly technical language), the evaluation could yield inaccurate or biased results. Furthermore, if generated texts introduce valid but creative or non-standard content, this approach may penalise them by classifying such deviations as contradictions or hallucinations, even when they provide accurate information. Future work will need to address these limitations across various domains.