Improving Molecule-Language Alignment with Hierarchical Graph Tokenization

Yongqiang Chen, Quanming Yao, Juzheng Zhang, James Cheng, Yatao Bian

Keywords: molecular-language alignment, large language models, hierarchical graph neural networks, tokenization, biomolecular studies, molecule

TL;DR: We present a new strategy that incorporates hierarchical graph information into supervised finetuning and instruction datasets for a better alignment of graph and language modalities.

Abstract:

Recently there has been a surge of interest in extending the success of large language models (LLMs) to graph modality, such as molecules. As LLMs are predominantly trained with 1D text data, most existing approaches adopt a graph neural network to represent a molecule as a series of node tokens and feed these tokens to LLMs for molecule-language alignment. Despite achieving some successes, existing approaches have overlooked the hierarchical structures that are inherent in molecules. Specifically, in molecular graphs, the high-order structural information contains rich semantics of molecular functional groups, which encode crucial biochemical functionalities of the molecules. We establish a simple benchmark showing that neglecting the hierarchical information in graph tokenization will lead to subpar molecule-language alignment and severe hallucination in generated outputs. To address this problem, we propose a novel strategy called HIerarchical GrapH Tokenization (HIGHT). HIGHT employs a hierarchical graph tokenizer that extracts and encodes the hierarchy of node, motif, and graph levels of informative tokens to improve the graph perception of LLMs. HIGHT also adopts an augmented molecule-language supervised fine-tuning dataset, enriched with the hierarchical graph information, to further enhance the molecule-language alignment. Extensive experiments on 14 molecule-centric benchmarks confirm the effectiveness of HIGHT in reducing hallucination by 40%, as well as significant improvements in various molecule-language downstream tasks.

Primary Area: learning on graphs and other geometries & topologies

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Submission Number: 7740