# L+M-24: Building a Dataset for Language + Molecules @ ACL 2024

arXiv: https://arxiv.org/abs/2403.00791 · HTML: https://mlsanity.com/paper/2403.00791/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 43 references that are in the map.
- First posted on arXiv 22 Feb 2024.

## Builds on (most cited first, 21 of 21)

- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [ChemCrow: Augmenting large-language models with chemistry tools](https://mlsanity.com/paper/2304.05376/) (2023)
- [Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models](https://mlsanity.com/paper/1811.12823/) (2018)
- [Translation between Molecules and Natural Language](https://mlsanity.com/paper/2204.11817/) (2022)
- [Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editing](https://mlsanity.com/paper/2212.10789/) (2022)
- [A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language](https://mlsanity.com/paper/2209.05481/) (2022)
- [MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter](https://mlsanity.com/paper/2310.12798/) (2023)
- [LlaSMol: Advancing Large Language Models for Chemistry with a Large-Scale, Comprehensive, High-Quality Instruction Tuning Dataset](https://mlsanity.com/paper/2402.09391/) (2024)
- [Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems](https://mlsanity.com/paper/2307.08423/) (2023)
- [BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations](https://mlsanity.com/paper/2310.07276/) (2023)
- [Unifying Molecular and Textual Representations via Multi-task Language Modelling](https://mlsanity.com/paper/2301.12586/) (2023)
- [InstructMol: Multi-Modal Integration for Building a Versatile and Reliable Molecular Assistant in Drug Discovery](https://mlsanity.com/paper/2311.16208/) (2023)
- [GIT-Mol: A Multi-modal Large Language Model for Molecular Science with Graph, Image, and Text](https://mlsanity.com/paper/2308.06911/) (2023)
- [MolXPT: Wrapping Molecules with Text for Generative Pre-training](https://mlsanity.com/paper/2305.10688/) (2023)
- [Developing ChemDFM as a large language foundation model for chemistry](https://mlsanity.com/paper/2401.14818/) (2024)
- [MolFM: A Multimodal Molecular Foundation Model](https://mlsanity.com/paper/2307.09484/) (2023)
- [Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language](https://mlsanity.com/paper/2303.03363/) (2023)
- [ChemReasoner: Heuristic Search over a Large Language Model's Knowledge Space using Quantum-Chemical Feedback](https://mlsanity.com/paper/2402.10980/) (2024)
- [Monte Carlo Thought Search: Large Language Model Querying for Complex Scientific Reasoning in Catalyst Design](https://mlsanity.com/paper/2310.14420/) (2023)
- [From Artificially Real to Real: Leveraging Pseudo Data from Large Language Models for Low-Resource Molecule Discovery](https://mlsanity.com/paper/2309.05203/) (2023)
- [3VL: Using Trees to Improve Vision-Language Models' Interpretability](https://mlsanity.com/paper/2312.17345/) (2023)

## Built on by (most cited first, 21 of 21)

- [Speak-to-Structure: Evaluating LLMs in Open-domain Natural Language-Driven Molecule Generation](https://mlsanity.com/paper/2412.14642/) (2024)
- [Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Survey](https://mlsanity.com/paper/2403.01528/) (2024)
- [Vector-ICL: In-context Learning with Continuous Vector Representations](https://mlsanity.com/paper/2410.05629/) (2024)
- [A Survey of Large Language Models for Text-Guided Molecular Discovery: from Molecule Generation to Optimization](https://mlsanity.com/paper/2505.16094/) (2025)
- [Can LLMs Generate Diverse Molecules? Towards Alignment with Structural Diversity](https://mlsanity.com/paper/2410.03138/) (2024)
- [MolLangBench: A Comprehensive Benchmark for Language-Prompted Molecular Structure Recognition, Editing, and Generation](https://mlsanity.com/paper/2505.15054/) (2025)
- [mCLM: A Modular Chemical Language Model that Generates Functional and Makeable Molecules](https://mlsanity.com/paper/2505.12565/) (2025)
- [KnowMol: Advancing Molecular Large Language Models with Multi-Level Chemical Knowledge](https://mlsanity.com/paper/2510.19484/) (2025)
- [ALMol: Aligned Language-Molecule Translation LLMs through Offline Preference Contrastive Optimisation](https://mlsanity.com/paper/2405.08619/) (2024)
- [MolEdit: Knowledge Editing for Multimodal Molecule Language Models](https://mlsanity.com/paper/2511.12770/) (2025)
- [BioMedGPT-Mol: Multi-task Learning for Molecular Understanding and Generation](https://mlsanity.com/paper/2512.04629/) (2025)
- [FARM: Enhancing Molecular Representations with Functional Group Awareness](https://mlsanity.com/paper/2410.02082/) (2024)
- [Improving Large Language Models with Concept-Aware Fine-Tuning](https://mlsanity.com/paper/2506.07833/) (2025)
- [Round-trip Reinforcement Learning: Self-Consistent Training for Better Chemical LLMs](https://mlsanity.com/paper/2510.01527/) (2025)
- [Less for More: Enhanced Feedback-aligned Mixed LLMs for Molecule Caption Generation and Fine-Grained NLI Evaluation](https://mlsanity.com/paper/2405.13984/) (2024)
- [MolGround: A Benchmark for Molecular Grounding](https://mlsanity.com/paper/2503.23668/) (2025)
- [RTMol: Rethinking Molecule-text Alignment in a Round-trip View](https://mlsanity.com/paper/2511.12135/) (2025)
- [Chemical Language Model Linker: blending text and molecules with modular adapters](https://mlsanity.com/paper/2410.20182/) (2024)
- [MolLangData: A Large-Scale Dataset for Molecular Structure-Language Description via a Rule-Regularized Method](https://mlsanity.com/paper/2602.02320/) (2026)
- [MMAI Gym for Science: Training Liquid Foundation Models for Drug Discovery](https://mlsanity.com/paper/2603.03517/) (2026)
- [TheBioCollection: Unified Pre-Training Scale LLM Corpus for Biology](https://mlsanity.com/paper/2607.08803/) (2026)

## Abstract (arXiv)

Language-molecule models have emerged as an exciting direction for molecular discovery and understanding. However, training these models is challenging due to the scarcity of molecule-language pair datasets. At this point, datasets have been released which are 1) small and scraped from existing databases, 2) large but noisy and constructed by performing entity linking on the scientific literature, and 3) built by converting property prediction datasets to natural language using templates. In this document, we detail the textitL+M-24 dataset, which has been created for the Language + Molecules Workshop shared task at ACL 2024. In particular, textitL+M-24 is designed to focus on three key benefits of natural language in molecule design: compositionality, functionality, and abstraction.