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    <abstract>Recent advancements in AI for biological research focus on integrating molecular data with natural language to accelerate drug discovery. However, the scarcity of high-quality annotations limits progress in this area. This paper introduces LA³, a Language-based Automatic Annotation Augmentation framework that leverages large language models to augment existing datasets, thereby improving AI training. We demonstrate the effectiveness of LA³ by creating an enhanced dataset, LaChEBI-20, where we systematically rewrite the annotations of molecules from an established dataset. These rewritten annotations preserve essential molecular information while providing more varied sentence structures and vocabulary. Using LaChEBI-20, we train LaMolT5 based on a benchmark architecture to learn the mapping between molecular representations and augmented annotations.Experimental results on text-based *de novo* molecule generation and molecule captioning demonstrate that LaMolT5 outperforms state-of-the-art models. Notably, incorporating LA³ leads to improvements of up to 301% over the benchmark architecture. Furthermore, we validate the effectiveness of LA³ notable applications in *image*, *text* and *graph* tasks, affirming its versatility and utility.</abstract>
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%0 Conference Proceedings
%T Automatic Annotation Augmentation Boosts Translation between Molecules and Natural Language
%A Zhong, Zhiqiang
%A Larsen, Simon Sataa-Yu
%A Guo, Haoyu
%A Tang, Tao
%A Zhou, Kuangyu
%A Mottin, Davide
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F zhong-etal-2025-automatic
%X Recent advancements in AI for biological research focus on integrating molecular data with natural language to accelerate drug discovery. However, the scarcity of high-quality annotations limits progress in this area. This paper introduces LA³, a Language-based Automatic Annotation Augmentation framework that leverages large language models to augment existing datasets, thereby improving AI training. We demonstrate the effectiveness of LA³ by creating an enhanced dataset, LaChEBI-20, where we systematically rewrite the annotations of molecules from an established dataset. These rewritten annotations preserve essential molecular information while providing more varied sentence structures and vocabulary. Using LaChEBI-20, we train LaMolT5 based on a benchmark architecture to learn the mapping between molecular representations and augmented annotations.Experimental results on text-based *de novo* molecule generation and molecule captioning demonstrate that LaMolT5 outperforms state-of-the-art models. Notably, incorporating LA³ leads to improvements of up to 301% over the benchmark architecture. Furthermore, we validate the effectiveness of LA³ notable applications in *image*, *text* and *graph* tasks, affirming its versatility and utility.
%R 10.18653/v1/2025.findings-naacl.345
%U https://aclanthology.org/2025.findings-naacl.345/
%U https://doi.org/10.18653/v1/2025.findings-naacl.345
%P 6177-6194

Markdown (Informal)

[Automatic Annotation Augmentation Boosts Translation between Molecules and Natural Language](https://aclanthology.org/2025.findings-naacl.345/) (Zhong et al., Findings 2025)

• Automatic Annotation Augmentation Boosts Translation between Molecules and Natural Language (Zhong et al., Findings 2025)

ACL

• Zhiqiang Zhong, Simon Sataa-Yu Larsen, Haoyu Guo, Tao Tang, Kuangyu Zhou, and Davide Mottin. 2025. Automatic Annotation Augmentation Boosts Translation between Molecules and Natural Language. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 6177–6194, Albuquerque, New Mexico. Association for Computational Linguistics.