  The intersection of chemistry and Artificial Intelligence (AI) is an active
area of research focused on accelerating scientific discovery. While using
large language models (LLMs) with scientific modalities has shown potential,
there are significant challenges to address, such as improving training
efficiency and dealing with the out-of-distribution problem. Focussing on the
task of automated language-molecule translation, we are the first to use
state-of-the art (SOTA) human-centric optimisation algorithms in the
cross-modal setting, successfully aligning cross-language-molecule modals. We
empirically show that we can augment the capabilities of scientific LLMs
without the need for extensive data or large models. We conduct experiments
using only 10% of the available data to mitigate memorisation effects
associated with training large models on extensive datasets. We achieve
significant performance gains, surpassing the best benchmark model trained on
extensive in-distribution data by a large margin and reach new SOTA levels.
Additionally we are the first to propose employing non-linear fusion for mixing
cross-modal LLMs which further boosts performance gains without increasing
training costs or data needs. Finally, we introduce a fine-grained,
domain-agnostic evaluation method to assess hallucination in LLMs and promote
responsible use.
