The potential for generating misleading or incorrect information poses significant ethical considerations in this work, particularly given the scientific context in which the language models are applied. If the models produce inaccurate captions or misrepresent molecular characteristics, it could lead to erroneous conclusions in research and applications that rely on these outputs. This risk is particularly critical in fields like chemistry, where precise data interpretation is vital for safety, compliance, and advancing scientific knowledge. Furthermore, the reliance on automated evaluations may not adequately catch nuanced errors that human experts would recognise, potentially allowing flawed outputs to go unchecked. Therefore, ensuring that the models maintain a high standard of accuracy and reliability is essential to prevent the dissemination of misinformation, which could undermine trust in automated systems and hinder scientific progress. Addressing these ethical concerns requires implementing robust validation mechanisms and continuously involving domain experts in the evaluation process to ensure the integrity of the generated content.