We evaluate our models on out-of-distribution data using established statistical-based metrics . Additionally, we use Natural Language Inference (NLI) models to assess generated text within the chemical domain. However, we argue that off-the-shelf NLI models are suboptimal for several reasons: a) they are trained on relatively short texts , while generated text may aggregate multiple content units that partially overlap with different sentences in the reference text ; b) they are limited by the data they were trained on, making them unreliable for unseen domains ; and c) they lack subsentence inference, hindering their ability to handle reordered content in generated text (see Fig. ). Thus we propose a novel atomic-level cross-NLI approach that addresses these issues. By decomposing reference and generated texts into atomic premises and hypotheses using an LLM, we calculate probability distributions of contradiction and entailment via an NLI model and finally apply row-wise operations to obtain novel hallucination and coverage metrics ( §3.3). Our findings and contributions are as follows: