# From Artificially Real to Real: Leveraging Pseudo Data from Large Language Models for Low-Resource Molecule Discovery arXiv: https://arxiv.org/abs/2309.05203 ยท HTML: https://mlsanity.com/paper/2309.05203/ ## Key facts - Cited by 11 papers in the map (data through 14 Oct 2026). - Builds on 26 of its 54 references that are in the map. - First posted on arXiv 11 Sep 2023. ## Builds on (most cited first, 26 of 26) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022) - [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019) - [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022) - [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023) - [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022) - [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019) - [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019) - [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015) - [Learning To Retrieve Prompts for In-Context Learning](https://mlsanity.com/paper/2112.08633/) (2021) - [MetaICL: Learning to Learn In Context](https://mlsanity.com/paper/2110.15943/) (2021) - [Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models](https://mlsanity.com/paper/1811.12823/) (2018) - [Promptagator: Few-shot Dense Retrieval From 8 Examples](https://mlsanity.com/paper/2209.11755/) (2022) - [WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation](https://mlsanity.com/paper/2201.05955/) (2022) - [Fr'echet ChemNet Distance: A metric for generative models for molecules in drug discovery](https://mlsanity.com/paper/1803.09518/) (2018) - [Generating Datasets with Pretrained Language Models](https://mlsanity.com/paper/2104.07540/) (2021) - [Translation between Molecules and Natural Language](https://mlsanity.com/paper/2204.11817/) (2022) - [HuaTuo: Tuning LLaMA Model with Chinese Medical Knowledge](https://mlsanity.com/paper/2304.06975/) (2023) - [Unifying Molecular and Textual Representations via Multi-task Language Modelling](https://mlsanity.com/paper/2301.12586/) (2023) - [Empowering Molecule Discovery for Molecule-Caption Translation with Large Language Models: A ChatGPT Perspective](https://mlsanity.com/paper/2306.06615/) (2023) - [MolXPT: Wrapping Molecules with Text for Generative Pre-training](https://mlsanity.com/paper/2305.10688/) (2023) - [Snippext: Semi-supervised Opinion Mining with Augmented Data](https://mlsanity.com/paper/2002.03049/) (2020) - [UniCoRN: Unified Cognitive Signal ReconstructioN bridging cognitive signals and human language](https://mlsanity.com/paper/2307.05355/) (2023) - [Knowledge-tuning Large Language Models with Structured Medical Knowledge Bases for Reliable Response Generation in Chinese](https://mlsanity.com/paper/2309.04175/) (2023) - [Evolution of transfer learning in natural language processing](https://mlsanity.com/paper/1910.07370/) (2019) - [Don't Ignore Dual Logic Ability of LLMs while Privatizing: A Data-Intensive Analysis in Medical Domain](https://mlsanity.com/paper/2309.04198/) (2023) ## Built on by (most cited first, 11 of 11) - [L+M-24: Building a Dataset for Language + Molecules @ ACL 2024](https://mlsanity.com/paper/2403.00791/) (2024) - [Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Survey](https://mlsanity.com/paper/2403.01528/) (2024) - [Geometry Informed Tokenization of Molecules for Language Model Generation](https://mlsanity.com/paper/2408.10120/) (2024) - [KnowMol: Advancing Molecular Large Language Models with Multi-Level Chemical Knowledge](https://mlsanity.com/paper/2510.19484/) (2025) - [MolTailor: Tailoring Chemical Molecular Representation to Specific Tasks via Text Prompts](https://mlsanity.com/paper/2401.11403/) (2024) - [Bridging Text and Molecule: A Survey on Multimodal Frameworks for Molecule](https://mlsanity.com/paper/2403.13830/) (2024) - [Large Language Models for Link Stealing Attacks Against Graph Neural Networks](https://mlsanity.com/paper/2406.16963/) (2024) - [Make Your Decision Convincing! A Unified Two-Stage Framework: Self-Attribution and Decision-Making](https://mlsanity.com/paper/2310.13610/) (2023) - [Training Text-to-Molecule Models with Context-Aware Tokenization](https://mlsanity.com/paper/2509.04476/) (2025) - [Chemical Language Model Linker: blending text and molecules with modular adapters](https://mlsanity.com/paper/2410.20182/) (2024) - [Resource-constrained Amazons chess decision framework integrating large language models and graph attention](https://mlsanity.com/paper/2603.10512/) (2026) ## Abstract (arXiv) Molecule discovery serves as a cornerstone in numerous scientific domains, fueling the development of new materials and innovative drug designs. Recent developments of in-silico molecule discovery have highlighted the promising results of cross-modal techniques, which bridge molecular structures with their descriptive annotations. However, these cross-modal methods frequently encounter the issue of data scarcity, hampering their performance and application. In this paper, we address the low-resource challenge by utilizing artificially-real data generated by Large Language Models (LLMs). We first introduce a retrieval-based prompting strategy to construct high-quality pseudo data, then explore the optimal method to effectively leverage this pseudo data. Experiments show that using pseudo data for domain adaptation outperforms all existing methods, while also requiring a smaller model scale, reduced data size and lower training cost, highlighting its efficiency. Furthermore, our method shows a sustained improvement as the volume of pseudo data increases, revealing the great potential of pseudo data in advancing low-resource cross-modal molecule discovery. Our code and data are available at https://github.com/SCIR-HI/ArtificiallyR2R.