{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/mol2lang-vlm-vision-and-text-guided","title":"Mol2Lang-VLM: Vision- and Text-Guided Generative Pre-trained Language Models for Advancing Molecule Captioning through Multimodal Fusion","arxiv_id":null,"date":"2024-08-15","proceeding":"Association for Computational Linguistics 2024 8","authors":["Duong Tran","Nhat Truong Pham","Nguyen Nguyen","and Balachandran Manavalan"],"abstract":"This paper introduces Mol2Lang-VLM, an enhanced method for refining generative pre-trained language models for molecule captioning using multimodal features to achieve more accurate caption generation. Our approach leverages the encoder and decoder blocks of the Transformer-based architecture by introducing third sub-layers into both. Specifically, we insert sub-layers in the encoder to fuse features from SELFIES strings and molecular images, while the decoder fuses features from SMILES strings and their corresponding descriptions. Moreover, cross multi-head attention is employed instead of common multi-head attention to enable the decoder to attend to the encoder’s output, thereby integrating the encoded contextual information for better and more accurate caption generation. Performance evaluation on the CheBI-20 and L+M-24 benchmark datasets demonstrates Mol2Lang-VLM’s superiority, achieving higher accuracy and quality in caption generation compared to existing methods. Our code and pre-processed data are available at https://github.com/nhattruongpham/mol-lang-bridge/tree/mol2lang/.","url_abs":"https://aclanthology.org/2024.langmol-1.12/","url_pdf":"https://aclanthology.org/2024.langmol-1.12.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"mol2lang-vlm-vision-and-text-guided","repo_url":"https://github.com/nhattruongpham/mol-lang-bridge","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"caption-generation","task_name":"Caption Generation"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"molecule-captioning","task_name":"Molecule Captioning"}],"methods":[{"method_slug":"attention","method_name":"Attention"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/molecule-captioning-on-chebi-20","task":"Molecule Captioning","dataset":"ChEBI-20","model":"Mol2Lang-VLM","rank_in_archive_order":10,"of":33,"metrics":{"BLEU-2":"61.2","BLEU-4":"52.7","METEOR":"63.3","ROUGE-1":"67.4","ROUGE-2":"53.2","ROUGE-L":"61.4","Text2Mol":"59.8"},"uses_additional_data":false},{"leaderboard":"/sota/molecule-captioning-on-l-m-24","task":"Molecule Captioning","dataset":"L+M-24","model":"Mol2Lang-VLM","rank_in_archive_order":1,"of":6,"metrics":{"BLEU-2":"77.7","BLEU-4":"56.3","METEOR":"74.1","ROUGE-1":"78.6","ROUGE-2":"59.1","ROUGE-L":"56.5"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}