mlsanityData to 14 Oct 2026

GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning

arXiv 2306.13089 ↗First posted 28 May 2023cs.LG cs.CL q-bio.BMcited by 70

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Abstract (arXiv)

Molecule property prediction has gained significant attention in recent years. The main bottleneck is the label insufficiency caused by expensive lab experiments. In order to alleviate this issue and to better leverage textual knowledge for tasks, this study investigates the feasibility of employing natural language instructions to accomplish molecule-related tasks in a zero-shot setting. We discover that existing molecule-text models perform poorly in this setting due to inadequate treatment of instructions and limited capacity for graphs. To overcome these issues, we propose GIMLET, which unifies language models for both graph and text data. By adopting generalized position embedding, our model is extended to encode both graph structures and instruction text without additional graph encoding modules. GIMLET also decouples encoding of the graph from tasks instructions in the attention mechanism, enhancing the generalization of graph features across novel tasks. We construct a dataset consisting of more than two thousand molecule tasks with corresponding instructions derived from task descriptions. We pretrain GIMLET on the molecule tasks along with instructions, enabling the model to transfer effectively to a broad range of tasks. Experimental results demonstrate that GIMLET significantly outperforms molecule-text baselines in instruction-based zero-shot learning, even achieving closed results to supervised GNN models on tasks such as toxcast and muv.

Key facts. Cited by 70 papers in the map (data through 14 Oct 2026). Builds on 70 of its 89 references that are in the map. First posted on arXiv 28 May 2023.

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70 of 89 references.

References · 70

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  3. 2020Language Models are Few-Shot Learnerscited by 32,419
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38 more
  1. 2022Scaling Instruction-Finetuned Language Modelscited by 3,024
  2. 2021BEiT: BERT Pre-Training of Image Transformerscited by 2,375
  3. 2021Multitask Prompted Training Enables Zero-Shot Task Generalizationcited by 1,470
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Citing papers per year: 2023: 8; 2024: 34; 2025: 19; 2026: 9.

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  1. 2024A Survey on Knowledge Distillation of Large Language Modelscited by 284
  2. 2023One for All: Towards Training One Graph Model for All Classification Taskscited by 243
  3. 2024Continual Learning of Large Language Models: A Comprehensive Surveycited by 234
  4. 2024G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answeringcited by 223
  5. 2023Large Language Models on Graphs: A Comprehensive Surveycited by 192
  6. 2023UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecastingcited by 144
  7. 2023Graph Foundation Models: Concepts, Opportunities and Challengescited by 103
  8. 2024Position: Graph Foundation Models are Already Herecited by 86
  9. 2023A Survey of Graph Meets Large Language Model: Progress and Future Directionscited by 85
  10. 2024A Survey of Large Language Models for Graphscited by 72
  11. 2023Graph Prompt Learning: A Comprehensive Survey and Beyondcited by 61
  12. 2024GOFA: A Generative One-For-All Model for Joint Graph Language Modelingcited by 57
38 more
  1. 2024Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insightscited by 44
  2. 2024How Can Large Language Models Understand Spatial-Temporal Data?cited by 33
  3. 2024ChemReasoner: Heuristic Search over a Large Language Model's Knowledge Space using Quantum-Chemical Feedbackcited by 33
  4. 2024Graph Machine Learning in the Era of Large Language Models (LLMs)cited by 33
  5. 2024Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Treescited by 32
  6. 2024Multimodal Large Language Models for Inverse Molecular Design with Retrosynthetic Planningcited by 29
  7. 2023Large Language Models as Topological Structure Enhancers for Text-Attributed Graphscited by 27
  8. 2024Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?cited by 23
  9. 2023Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materialscited by 22
  10. 2024LLaMo: Large Language Model-based Molecular Graph Assistantcited by 22
  11. 2025RAG-Enhanced Collaborative LLM Agents for Drug Discoverycited by 18
  12. 2024Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Surveycited by 17
  13. 2024A Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applicationscited by 17
  14. 2024HIGHT: Hierarchical Graph Tokenization for Molecule-Language Alignmentcited by 17
  15. 2024From Words to Molecules: A Survey of Large Language Models in Chemistrycited by 14
  16. 2024Can Graph Neural Networks Learn Language with Extremely Weak Text Supervision?cited by 13
  17. 2025Graph2text or Graph2token: A Perspective of Large Language Models for Graph Learningcited by 12
  18. 2024Learning on Graphs with Large Language Models(LLMs): A Deep Dive into Model Robustnesscited by 11
  19. 2024Geometry Informed Tokenization of Molecules for Language Model Generationcited by 10
  20. 2024TAGLAS: An atlas of text-attributed graph datasets in the era of large graph and language modelscited by 9
  21. 2024Advancing Molecular Graph-Text Pre-training via Fine-grained Alignmentcited by 9
  22. 2025The Quest for Efficient Reasoning: A Data-Centric Benchmark to CoT Distillationcited by 9
  23. 20243M-Diffusion: Latent Multi-Modal Diffusion for Language-Guided Molecular Structure Generationcited by 8
  24. 2025Model Generalization on Text Attribute Graphs: Principles with Large Language Modelscited by 8
  25. 2025mCLM: A Modular Chemical Language Model that Generates Functional and Makeable Moleculescited by 8
  26. 2025Towards Graph Foundation Models: A Transferability Perspectivecited by 7
  27. 2025KnowMol: Advancing Molecular Large Language Models with Multi-Level Chemical Knowledgecited by 7
  28. 2024A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt Learningcited by 6
  29. 2025Large Language Models Meet Text-Attributed Graphs: A Survey of Integration Frameworks and Applicationscited by 6
  30. 2024GUNDAM: Aligning Large Language Models with Graph Understandingcited by 5
  31. 2025HeTGB: A Comprehensive Benchmark for Heterophilic Text-Attributed Graphscited by 5
  32. 2026Structure-Centric Graph Foundation Model via Geometric Basescited by 5
  33. 2024Bridging Text and Molecule: A Survey on Multimodal Frameworks for Moleculecited by 4
  34. 2024Instruction-Based Molecular Graph Generation with Unified Text-Graph Diffusion Modelcited by 3
  35. 2024Strada-LLM: Graph LLM for traffic predictioncited by 3
  36. 2024Large Language Models Meet Graph Neural Networks: A Perspective of Graph Miningcited by 3
  37. 2025Data-centric Federated Graph Learning with Large Language Modelscited by 3
  38. 2025Integrating Structural and Semantic Signals in Text-Attributed Graphs with BiGTexcited by 3

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