  The field of chemistry and Artificial Intelligence (AI) intersection is an
area of active research that aims to accelerate scientific discovery. The
integration of large language models (LLMs) with scientific modalities has
shown significant promise in this endeavour. However, challenges persist in
effectively addressing training efficacy and the out-of-distribution problem,
particularly as existing approaches rely on larger models and datasets. In this
context, we focus on machine language-molecule translation and deploy a novel
training approach called contrastive preference optimisation, which avoids
generating translations that are merely adequate but not perfect. To ensure
generalisability and mitigate memorisation effects, we conduct experiments
using only 10% of the data. Our results demonstrate that our models achieve up
to a 32% improvement compared to counterpart models. Finally, we introduce a
fine-grained, domain-agnostic evaluation method to assess hallucination in LLMs
and promote responsible use.
