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. We also introduce a
scalable fine-grained evaluation methodology that accommodates responsibility.

化学和人工智能的交叉领域是一个积极研究的领域，旨在加速科学发现；该研究聚焦于机器翻译化学语言和分子模型，并采用一种新的训练方法，通过对比优化来提高模型性能。