UniMoT: Unified Molecule-Text Language Model with Discrete Token Representation

Juzheng Zhang, Yatao Bian, Yongqiang Chen, Quanming Yao

Keywords: Large Language Models, Tokenization, Molecule Generation, Molecule Comprehension, Multi-modal Learning

Abstract:

The remarkable success of Large Language Models (LLMs) across diverse tasks has driven the research community to extend their capabilities to molecular applications. However, most molecular LLMs employ adapter-based architectures that do not treat molecule and text modalities equally and lack a supervision signal for the molecule modality. To address these issues, we introduce UniMoT, a Unified Molecule-Text LLM adopting a tokenizer-based architecture that expands the vocabulary of LLM with molecule tokens.
Specifically, we introduce a Vector Quantization-driven tokenizer that incorporates a Q-Former to bridge the modality gap between molecule and text. This tokenizer transforms molecules into sequences of molecule tokens with causal dependency, encapsulating high-level molecular and textual information.
Equipped with this tokenizer, UniMoT can unify molecule and text modalities under a shared token representation and an autoregressive training paradigm.
It can interpret molecules as a foreign language and generate them as text.
Following a four-stage training scheme, UniMoT emerges as a multi-modal generalist capable of performing both molecule-to-text and text-to-molecule tasks. Extensive experiments demonstrate that UniMoT achieves state-of-the-art performance across a wide range of molecule comprehension and generation tasks.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

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Submission Number: 9140