We conduct experiments training Meditron on the benchmark L+M-24 dataset, using only 10% of the data for training, and evaluate on out-of-distribution data (see Appx. B for details). For alignment finetuning, we create synthetic dispreferred outputs generated by MolT5 . Our training, validation, and test sets contain approximately 12.7k, 3.4k, and 3k samples, respectively. For evaluation, we use established statistical metrics (see Appx. E), along with our atomic-level cross-NLI evaluation method ( § 3.3). We compare our models thus obtained against established ones trained on extensive data. TxtChem-T5 databases using DPO, achieving GPT-4-level results. Meditron , a 7B model, is fine-tuned on the entire L+M-24 dataset. Lastly, we fine-tune Meditron with SFT using only 10% of the training data. We leave all the implementation details in Appx. G.