Our aim is to develop a method that operates at the right level of granularity, precisely capturing small distinctions and subtle nuances in captions, ensuring reliable evaluation. Atomic-level cross-NLI evaluation uses a LLM and an NLI model to assess relationships between generated and reference captions. The process begins with an LLM decomposing a (reference, generated) pair into atomic premises {P i } N i=1 and hypotheses {H j } L j=1 , where each atomic unit conveys a single piece of information (see Appx. C). An NLI model then constructs probabilistic distributions of entailment and contradiction by considering all possible combinations of premises and hypotheses. Finally, pooling operators match atomic hypotheses and premises in terms of both factual correctness, i.e., hallucination, and completeness, i.e., coverage. Fig. illustrates this process. Hallucination we define here as the introduction of information not present in the reference text. Given {(P i , H j )}, the NLI model constructs a contradiction probability distribution for each atomic hypothesis against all premises, such as p j,i = (C j,i |P i , H j ). This results in an M L×N matrix of contradiction probabilities C j,i (see Fig. ). To measure hallucination, we apply min row-wise pooling and average the matching probabilities to compute the score by the formula: