# CurateGPT: A flexible language-model assisted biocuration tool

arXiv: https://arxiv.org/abs/2411.00046 · HTML: https://mlsanity.com/paper/2411.00046/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 42 references that are in the map.
- First posted on arXiv 29 Oct 2024.

## Builds on (most cited first, 13 of 13)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Large Language Models Struggle to Learn Long-Tail Knowledge](https://mlsanity.com/paper/2211.08411/) (2022)
- [The Programmer's Assistant: Conversational Interaction with a Large Language Model for Software Development](https://mlsanity.com/paper/2302.07080/) (2023)
- [LLMs Accelerate Annotation for Medical Information Extraction](https://mlsanity.com/paper/2312.02296/) (2023)
- [Structured prompt interrogation and recursive extraction of semantics (SPIRES): A method for populating knowledge bases using zero-shot learning](https://mlsanity.com/paper/2304.02711/) (2023)
- [PubTator 3.0: an AI-powered Literature Resource for Unlocking Biomedical Knowledge](https://mlsanity.com/paper/2401.11048/) (2024)
- [How well do LLMs cite relevant medical references? An evaluation framework and analyses](https://mlsanity.com/paper/2402.02008/) (2024)
- [Dynamic Retrieval Augmented Generation of Ontologies using Artificial Intelligence (DRAGON-AI)](https://mlsanity.com/paper/2312.10904/) (2023)
- [A Simple Standard for Sharing Ontological Mappings (SSSOM)](https://mlsanity.com/paper/2112.07051/) (2021)
- [Gene Set Summarization using Large Language Models](https://mlsanity.com/paper/2305.13338/) (2023)
- [Use of a Structured Knowledge Base Enhances Metadata Curation by Large Language Models](https://mlsanity.com/paper/2404.05893/) (2024)
- [The Vertebrate Breed Ontology: Towards Effective Breed Data Standardization](https://mlsanity.com/paper/2406.02623/) (2024)

## Built on by (most cited first, 3 of 3)

- [Chemical classification program synthesis using generative artificial intelligence](https://mlsanity.com/paper/2505.18470/) (2025)
- [FlyAOC: Evaluating Agentic Ontology Curation of Drosophila Scientific Knowledge Bases](https://mlsanity.com/paper/2602.09163/) (2026)
- [Building AI-Ready Data Systems for Space Life Sciences, Aerospace Medicine, and Deep Space Exploration](https://mlsanity.com/paper/2606.28856/) (2026)

## Abstract (arXiv)

Effective data-driven biomedical discovery requires data curation: a time-consuming process of finding, organizing, distilling, integrating, interpreting, annotating, and validating diverse information into a structured form suitable for databases and knowledge bases. Accurate and efficient curation of these digital assets is critical to ensuring that they are FAIR, trustworthy, and sustainable. Unfortunately, expert curators face significant time and resource constraints. The rapid pace of new information being published daily is exceeding their capacity for curation. Generative AI, exemplified by instruction-tuned large language models (LLMs), has opened up new possibilities for assisting human-driven curation. The design philosophy of agents combines the emerging abilities of generative AI with more precise methods. A curator's tasks can be aided by agents for performing reasoning, searching ontologies, and integrating knowledge across external sources, all efforts otherwise requiring extensive manual effort. Our LLM-driven annotation tool, CurateGPT, melds the power of generative AI together with trusted knowledge bases and literature sources. CurateGPT streamlines the curation process, enhancing collaboration and efficiency in common workflows. Compared to direct interaction with an LLM, CurateGPT's agents enable access to information beyond that in the LLM's training data and they provide direct links to the data supporting each claim. This helps curators, researchers, and engineers scale up curation efforts to keep pace with the ever-increasing volume of scientific data.