In this work, we address limitations of scientific language models that rely on extensive training. Focusing on molecule caption generation, we propose synergies between model merging and alignment fine-tuning with minimal training to enhance chemical language models. Our experiments show that while alignment fine-tuning performs poorly, incorporating model merging significantly outperforms extensively trained models on out-of-distribution data, offering a cost-effective approach that relies less on human-labelled data. Furthermore, we propose an atomic-level cross- NLI evaluation to overcome limitations of widely used NLI evaluation methods, which lack appropriate granularity. Our method provides valuable insight into performance interpretability and effectively handles multiple content units, where existing NLI methods consistently misalign with assessment criteria.