@inproceedings{li-etal-2026-gla,
    title = "{GLA}: Grounding Large Language Models in Molecular Hierarchy for Chemical Understanding",
    author = "Li, Yingxu  and
      Zeng, Jingjie  and
      Wang, Zekun  and
      Lin, Hongfei  and
      Yang, Liang",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1515/",
    pages = "30311--30323",
    ISBN = "979-8-89176-395-1",
    abstract = "Conventional Euclidean geometries lead to structural distortion and entangle core pharmacophoric identities with peripheral groups. Existing molecule-language models, relying on linear or uniform encodings, often obscure the hierarchical organization of chemical semantics. To address this, we propose Geometric-Language Alignment (GLA), a framework integrating intrinsic molecular topology into large language models. GLA employs a mixed-curvature encoder that adaptively learns geometric representations through a gating mechanism. These representations are aligned with text via a dual-view contrastive objective and injected into a frozen language model. Experiments on cross-modal retrieval, captioning, and property prediction benchmarks show GLA consistently improves performance over baselines, suggesting that modeling geometric heterogeneity enhances the grounding between molecular structure and chemical language."
}