Summary of Unimot: Unified Molecule-text Language Model with Discrete Token Representation, by Juzheng Zhang et al.
UniMoT: Unified Molecule-Text Language Model with Discrete Token Representation
by Juzheng Zhang, Yatao Bian, Yongqiang Chen, Quanming Yao
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The introduction of Unified Molecule-Text Large Language Models (LLMs) aims to extend the capabilities of LLMs to molecular applications. The proposed architecture, UniMoT, addresses issues in current molecular LLMs by introducing a tokenizer-based approach that treats molecule and text modalities equally. This is achieved through a Vector Quantization-driven tokenizer that incorporates a Q-Former to bridge the modality gap between molecule and text. UniMoT can unify molecule and text modalities under a shared token representation and an autoregressive training paradigm, enabling it to interpret molecules as a foreign language and generate them as text. The paper demonstrates state-of-the-art performance across various molecule comprehension and generation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary UniMoT is a new way to use big language models for chemistry. Right now, these models are great at understanding and generating human language, but they’re not very good at working with molecules. To fix this, the researchers created UniMoT, which can understand and generate molecule information just like it understands and generates text. This helps chemists and others work with molecules in a more powerful way. |
Keywords
» Artificial intelligence » Autoregressive » Quantization » Token » Tokenizer
