LLaMo: Groot taalmodelgebaseerd moleculair grafiekhulpmiddel

LLaMo: Large Language Model-based Molecular Graph Assistant

October 31, 2024

Auteurs: Jinyoung Park, Minseong Bae, Dohwan Ko, Hyunwoo J. Kim

cs.AI

Samenvatting

Grote Taalmodellen (LLM's) hebben opmerkelijke generalisatie- en instructievolgcapaciteiten aangetoond met instructieafstemming. De vooruitgang in LLM's en instructieafstemming heeft geleid tot de ontwikkeling van Grote Visie-Taalmodellen (LVLM's). Echter is de bekwaamheid van de LLM's en instructieafstemming minder onderzocht in het moleculaire domein. Daarom stellen we LLaMo voor: Groot Taalmodel-gebaseerde Moleculaire grafiekassistent, dat een end-to-end getraind groot moleculair grafiek-taalmodel is. Om de discrepantie tussen de taal- en grafiekmodaliteiten te overbruggen, presenteren we de meerlaagse grafiekprojector die grafiekrepresentaties omzet in grafiektokens door de uitvoerrepresentaties van elke GNN-laag en motiefrepresentaties te abstraheren met het kruislingse-aandachtmechanisme. We introduceren ook machinaal gegenereerde moleculaire grafiekinstructiedata om het grote moleculaire grafiek-taalmodel te instrueren voor algemene moleculaire en taalbegrip. Onze uitgebreide experimenten tonen aan dat LLaMo de beste prestaties laat zien op diverse taken, zoals moleculaire beschrijvingsgeneratie, eigenschapvoorspelling en IUPAC-naamvoorspelling. De code van LLaMo is beschikbaar op https://github.com/mlvlab/LLaMo.

English

Large Language Models (LLMs) have demonstrated remarkable generalization and instruction-following capabilities with instruction tuning. The advancements in LLMs and instruction tuning have led to the development of Large Vision-Language Models (LVLMs). However, the competency of the LLMs and instruction tuning have been less explored in the molecular domain. Thus, we propose LLaMo: Large Language Model-based Molecular graph assistant, which is an end-to-end trained large molecular graph-language model. To bridge the discrepancy between the language and graph modalities, we present the multi-level graph projector that transforms graph representations into graph tokens by abstracting the output representations of each GNN layer and motif representations with the cross-attention mechanism. We also introduce machine-generated molecular graph instruction data to instruction-tune the large molecular graph-language model for general-purpose molecule and language understanding. Our extensive experiments demonstrate that LLaMo shows the best performance on diverse tasks, such as molecular description generation, property prediction, and IUPAC name prediction. The code of LLaMo is available at https://github.com/mlvlab/LLaMo.

Summary

AI-Generated Summary

Paper Overview

LLaMo is a Large Language Model-based Molecular graph assistant that excels in molecular tasks by integrating a graph encoder, multi-level graph projector, and a large language model. It outperforms existing models in molecular description generation, property prediction, and IUPAC name prediction, showcasing its superiority in both generalist and specialist settings.

Core Contribution

Integration of a graph encoder, multi-level graph projector, and a large language model for instruction-following responses in the molecular domain.
Novel multi-level graph projector capturing multi-hop graph information by leveraging node representations from all layers of a GNN.
Two-stage training pipeline involving graph encoder training and LLM fine-tuning using LoRA.
Superior performance in molecular tasks like molecule description generation, property prediction, and IUPAC name prediction compared to existing LLM-based models.

Research Context

LLaMo addresses the need for enhanced instruction-following capabilities in molecular tasks by leveraging a multi-level graph projector and GPT-generated instruction-following data. It builds upon existing research in molecular modeling and language models, offering a comprehensive solution for accurate and informative molecule descriptions.

Keywords

Large Language Model, Molecular Graph, Graph Encoder, Multi-level Graph Projector, Graph Neural Networks, Instruction-following Responses, Molecular Description Generation, Property Prediction, IUPAC Name Prediction

Background

The research background of LLaMo involves the necessity for improved molecular modeling through language models. The study aims to bridge the gap in existing literature by introducing a novel approach that combines molecular graphs, text tokens, and SMILES representation in a large language model for enhanced instruction-following responses.

Research Gap

Lack of efficient instruction-following models in the molecular domain.
Limited integration of graph encoders and large language models for molecular tasks.
Insufficient exploration of multi-level graph projectors for capturing detailed molecular information.

Technical Challenges

Data leakage due to uncertainty in data exclusivity for LLM pretraining and testing.
Memory and computational costs associated with LLM-based models.
Hallucination issues inherited from LLMs affecting model performance.

Prior Approaches

Existing solutions lack the comprehensive integration of graph encoders, multi-level graph projectors, and large language models for instruction-following responses in molecular tasks. Limited emphasis on leveraging GPT-generated data for instruction-tuning.

Methodology

The methodology of LLaMo involves a graph encoder, multi-level graph projector, and large language model for instruction-following responses in molecular tasks. The model undergoes two-stage training, focusing on graph encoder training and LLM fine-tuning using LoRA.

Theoretical Foundation

Utilization of Graph Neural Networks for updating node representations and a multi-level graph projector for capturing multi-hop graph information. Integration of large language models for instruction-following capabilities.

Technical Architecture

Graph encoder utilizing GNNs for iterative node representation updates.
Multi-level graph projector aligning node representations with the language model.
Backbone large language model for generating instruction-following responses.

Implementation Details

Usage of PyTorch, PyTorch Geometric, Huggingface transformers, and GIN for implementation.
Specific optimization parameters and training schedules for model training.
Leveraging GPT-4 for generating multi-turn conversation datasets for instruction-tuning.

Innovation Points

Introduction of a multi-level graph projector for capturing detailed molecular information.
Effective instruction-tuning using GPT-generated data for enhancing model performance.
Superior performance in molecular tasks due to the comprehensive integration of graph encoders and large language models.

Experimental Validation

LLaMo is experimentally validated for tasks like molecule description generation, IUPAC name prediction, and property prediction, showcasing its superior performance compared to existing models. The evaluation involves specific configurations, metrics, and comparative analyses.

Setup

Training the multi-level graph projector with molecule-description pairs from datasets like PubChem.
Fine-tuning the language model using various datasets and GPT-generated instruction-following data.
Evaluation on tasks like molecular description generation, IUPAC name prediction, and property prediction.

Metrics

Evaluation metrics include BLEU and METEOR for text generation tasks.
MAE is used for property question answering tasks.

Results

Superior performance of LLaMo on molecular tasks compared to baselines.
Detailed experimental settings with specific implementation details and optimization parameters.

Comparative Analysis

Outperformance of LLaMo in chemical reaction tasks compared to existing models.
Benchmarking against LLM-based generalist models, molecule instruction-tuned models, and specialist models like MolCA.

Impact and Implications

LLaMo's impact lies in its superior performance in molecular tasks, although it faces limitations such as data leakage and computational costs. The model's broader implications include its wide applicability to various molecule-related tasks and potential biases in output.

Key Findings

Enhanced performance in molecular description generation, IUPAC name prediction, and property prediction.
Effective instruction-tuning with GPT-generated data for improved instruction-following capabilities.
Superiority over existing models in both generalist and specialist settings.

Limitations

Data leakage concerns due to uncertainty in data exclusivity.
Computational costs and memory requirements.
Potential biases in model output and environmental impact due to CO2 emissions during LLM training.

Future Directions

Addressing data leakage issues through more stringent data handling protocols.
Mitigating computational costs through optimization strategies.
Exploring methods to reduce biases in model output and environmental impact.

Practical Significance

LLaMo's applications in accurate and informative molecule description generation.
Potential for advancements in property prediction and IUPAC name generation in chemistry and biology fields.

References

The paper acknowledges related works in the fields of molecular modeling and language models.

Uitgelichte Papers

DeepSeek-R1: Het stimuleren van redeneervermogen in LLM's via Reinforcement Learning
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

DeepSeek-AI, Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, Xiaokang Zhang, Xingkai Yu, Yu Wu, Z. F. Wu, Zhibin Gou, Zhihong Shao, Zhuoshu Li, Ziyi Gao, Aixin Liu, Bing Xue, Bingxuan Wang, Bochao Wu, Bei Feng, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, Damai Dai, Deli Chen, Dongjie Ji, Erhang Li, Fangyun Lin, Fucong Dai, Fuli Luo, Guangbo Hao, Guanting Chen, Guowei Li, H. Zhang, Han Bao, Hanwei Xu, Haocheng Wang, Honghui Ding, Huajian Xin, Huazuo Gao, Hui Qu, Hui Li, Jianzhong Guo, Jiashi Li, Jiawei Wang, Jingchang Chen, Jingyang Yuan, Junjie Qiu, Junlong Li, J. L. Cai, Jiaqi Ni, Jian Liang, Jin Chen, Kai Dong, Kai Hu, Kaige Gao, Kang Guan, Kexin Huang, Kuai Yu, Lean Wang, Lecong Zhang, Liang Zhao, Litong Wang, Liyue Zhang, Lei Xu, Leyi Xia, Mingchuan Zhang, Minghua Zhang, Minghui Tang, Meng Li, Miaojun Wang, Mingming Li, Ning Tian, Panpan Huang, Peng Zhang, Qiancheng Wang, Qinyu Chen, Qiushi Du, Ruiqi Ge, Ruisong Zhang, Ruizhe Pan, Runji Wang, R. J. Chen, R. L. Jin, Ruyi Chen, Shanghao Lu, Shangyan Zhou, Shanhuang Chen, Shengfeng Ye, Shiyu Wang, Shuiping Yu, Shunfeng Zhou, Shuting Pan, S. S. Li, Shuang Zhou, Shaoqing Wu, Shengfeng Ye, Tao Yun, Tian Pei, Tianyu Sun, T. Wang, Wangding Zeng, Wanjia Zhao, Wen Liu, Wenfeng Liang, Wenjun Gao, Wenqin Yu, Wentao Zhang, W. L. Xiao, Wei An, Xiaodong Liu, Xiaohan Wang, Xiaokang Chen, Xiaotao Nie, Xin Cheng, Xin Liu, Xin Xie, Xingchao Liu, Xinyu Yang, Xinyuan Li, Xuecheng Su, Xuheng Lin, X. Q. Li, Xiangyue Jin, Xiaojin Shen, Xiaosha Chen, Xiaowen Sun, Xiaoxiang Wang, Xinnan Song, Xinyi Zhou, Xianzu Wang, Xinxia Shan, Y. K. Li, Y. Q. Wang, Y. X. Wei, Yang Zhang, Yanhong Xu, Yao Li, Yao Zhao, Yaofeng Sun, Yaohui Wang, Yi Yu, Yichao Zhang, Yifan Shi, Yiliang Xiong, Ying He, Yishi Piao, Yisong Wang, Yixuan Tan, Yiyang Ma, Yiyuan Liu, Yongqiang Guo, Yuan Ou, Yuduan Wang, Yue Gong, Yuheng Zou, Yujia He, Yunfan Xiong, Yuxiang Luo, Yuxiang You, Yuxuan Liu, Yuyang Zhou, Y. X. Zhu, Yanhong Xu, Yanping Huang, Yaohui Li, Yi Zheng, Yuchen Zhu, Yunxian Ma, Ying Tang, Yukun Zha, Yuting Yan, Z. Z. Ren, Zehui Ren, Zhangli Sha, Zhe Fu, Zhean Xu, Zhenda Xie, Zhengyan Zhang, Zhewen Hao, Zhicheng Ma, Zhigang Yan, Zhiyu Wu, Zihui Gu, Zijia Zhu, Zijun Liu, Zilin Li, Ziwei Xie, Ziyang Song, Zizheng Pan, Zhen Huang, Zhipeng Xu, Zhongyu Zhang, Zhen Zhang•Jan 22, 2025•3685

Technisch Rapport Qwen2.5
Qwen2.5 Technical Report

Qwen, An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, Huan Lin, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang, Jingren Zhou, Junyang Lin, Kai Dang, Keming Lu, Keqin Bao, Kexin Yang, Le Yu, Mei Li, Mingfeng Xue, Pei Zhang, Qin Zhu, Rui Men, Runji Lin, Tianhao Li, Tingyu Xia, Xingzhang Ren, Xuancheng Ren, Yang Fan, Yang Su, Yichang Zhang, Yu Wan, Yuqiong Liu, Zeyu Cui, Zhenru Zhang, Zihan Qiu•Dec 19, 2024•36311

MiniMax-01: Schalen van Foundation Modellen met Bliksem Aandacht
MiniMax-01: Scaling Foundation Models with Lightning Attention

MiniMax, Aonian Li, Bangwei Gong, Bo Yang, Boji Shan, Chang Liu, Cheng Zhu, Chunhao Zhang, Congchao Guo, Da Chen, Dong Li, Enwei Jiao, Gengxin Li, Guojun Zhang, Haohai Sun, Houze Dong, Jiadai Zhu, Jiaqi Zhuang, Jiayuan Song, Jin Zhu, Jingtao Han, Jingyang Li, Junbin Xie, Junhao Xu, Junjie Yan, Kaishun Zhang, Kecheng Xiao, Kexi Kang, Le Han, Leyang Wang, Lianfei Yu, Liheng Feng, Lin Zheng, Linbo Chai, Long Xing, Meizhi Ju, Mingyuan Chi, Mozhi Zhang, Peikai Huang, Pengcheng Niu, Pengfei Li, Pengyu Zhao, Qi Yang, Qidi Xu, Qiexiang Wang, Qin Wang, Qiuhui Li, Ruitao Leng, Shengmin Shi, Shuqi Yu, Sichen Li, Songquan Zhu, Tao Huang, Tianrun Liang, Weigao Sun, Weixuan Sun, Weiyu Cheng, Wenkai Li, Xiangjun Song, Xiao Su, Xiaodong Han, Xinjie Zhang, Xinzhu Hou, Xu Min, Xun Zou, Xuyang Shen, Yan Gong, Yingjie Zhu, Yipeng Zhou, Yiran Zhong, Yongyi Hu, Yuanxiang Fan, Yue Yu, Yufeng Yang, Yuhao Li, Yunan Huang, Yunji Li, Yunpeng Huang, Yunzhi Xu, Yuxin Mao, Zehan Li, Zekang Li, Zewei Tao, Zewen Ying, Zhaoyang Cong, Zhen Qin, Zhenhua Fan, Zhihang Yu, Zhuo Jiang, Zijia Wu•Jan 14, 2025•2836