CurateGPT: A flexible language-model assisted biocuration tool
- URL: http://arxiv.org/abs/2411.00046v1
- Date: Tue, 29 Oct 2024 20:00:04 GMT
- Title: CurateGPT: A flexible language-model assisted biocuration tool
- Authors: Harry Caufield, Carlo Kroll, Shawn T O'Neil, Justin T Reese, Marcin P Joachimiak, Harshad Hegde, Nomi L Harris, Madan Krishnamurthy, James A McLaughlin, Damian Smedley, Melissa A Haendel, Peter N Robinson, Christopher J Mungall,
- Abstract summary: Generative AI has opened up new possibilities for assisting human-driven curation.
CurateGPT streamlines the curation process, enhancing collaboration and efficiency in common.
This helps curators, researchers, and engineers scale up curation efforts to keep pace with the ever-increasing volume of scientific data.
- Score: 0.6425885600880427
- License:
- Abstract: 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.
Related papers
- Automating Knowledge Discovery from Scientific Literature via LLMs: A Dual-Agent Approach with Progressive Ontology Prompting [59.97247234955861]
We introduce a novel framework based on large language models (LLMs) that combines a progressive prompting algorithm with a dual-agent system, named LLM-Duo.
Our method identifies 2,421 interventions from 64,177 research articles in the speech-language therapy domain.
arXiv Detail & Related papers (2024-08-20T16:42:23Z) - Human-artificial intelligence teaming for scientific information extraction from data-driven additive manufacturing research using large language models [3.0061386772253784]
Data-driven research in Additive Manufacturing (AM) has gained significant success in recent years.
This has led to a plethora of scientific literature to emerge.
It requires substantial effort and time to extract scientific information from these works.
We propose a framework that enables collaboration between AM and AI experts to continuously extract scientific information from data-driven AM literature.
arXiv Detail & Related papers (2024-07-26T15:43:52Z) - AvaTaR: Optimizing LLM Agents for Tool Usage via Contrastive Reasoning [93.96463520716759]
Large language model (LLM) agents have demonstrated impressive capabilities in utilizing external tools and knowledge to boost accuracy and hallucinations.
Here, we introduce AvaTaR, a novel and automated framework that optimize an LLM agent to effectively leverage provided tools, improving performance on a given task.
arXiv Detail & Related papers (2024-06-17T04:20:02Z) - Self-Tuning: Instructing LLMs to Effectively Acquire New Knowledge through Self-Teaching [67.11497198002165]
Large language models (LLMs) often struggle to provide up-to-date information due to their one-time training.
Motivated by the remarkable success of the Feynman Technique in efficient human learning, we introduce Self-Tuning.
arXiv Detail & Related papers (2024-06-10T14:42:20Z) - BiomedRAG: A Retrieval Augmented Large Language Model for Biomedicine [19.861178160437827]
Large Language Models (LLMs) have swiftly emerged as vital resources for different applications in the biomedical and healthcare domains.
textscBiomedRAG attains superior performance across 5 biomedical NLP tasks.
textscBiomedRAG outperforms other triple extraction systems with micro-F1 scores of 81.42 and 88.83 on GIT and ChemProt corpora, respectively.
arXiv Detail & Related papers (2024-05-01T12:01:39Z) - Autonomous LLM-driven research from data to human-verifiable research papers [0.0]
We build an automation platform that guides interacting through complete stepwise process.
In mode provided annotated data alone, datapaper raised hypotheses, designed plans, wrote and interpreted analysis codes, generated and interpreted results.
We demonstrate potential for AI-driven acceleration of scientific discovery while enhancing traceability, transparency and verifiability.
arXiv Detail & Related papers (2024-04-24T23:15:49Z) - Agent-based Learning of Materials Datasets from Scientific Literature [0.0]
We develop a chemist AI agent, powered by large language models (LLMs), to create structured datasets from natural language text.
Our chemist AI agent, Eunomia, can plan and execute actions by leveraging the existing knowledge from decades of scientific research articles.
arXiv Detail & Related papers (2023-12-18T20:29:58Z) - LLMs Accelerate Annotation for Medical Information Extraction [7.743388571513413]
We propose an approach that combines Large Language Models (LLMs) with human expertise to create an efficient method for generating ground truth labels for medical text annotation.
We rigorously evaluate our method on a medical information extraction task, demonstrating that our approach not only substantially cuts down on human intervention but also maintains high accuracy.
arXiv Detail & Related papers (2023-12-04T19:26:13Z) - Self-Verification Improves Few-Shot Clinical Information Extraction [73.6905567014859]
Large language models (LLMs) have shown the potential to accelerate clinical curation via few-shot in-context learning.
They still struggle with issues regarding accuracy and interpretability, especially in mission-critical domains such as health.
Here, we explore a general mitigation framework using self-verification, which leverages the LLM to provide provenance for its own extraction and check its own outputs.
arXiv Detail & Related papers (2023-05-30T22:05:11Z) - Retrieval-Augmented and Knowledge-Grounded Language Models for Faithful Clinical Medicine [68.7814360102644]
We propose the Re$3$Writer method with retrieval-augmented generation and knowledge-grounded reasoning.
We demonstrate the effectiveness of our method in generating patient discharge instructions.
arXiv Detail & Related papers (2022-10-23T16:34:39Z) - BERT WEAVER: Using WEight AVERaging to enable lifelong learning for
transformer-based models in biomedical semantic search engines [49.75878234192369]
We present WEAVER, a simple, yet efficient post-processing method that infuses old knowledge into the new model.
We show that applying WEAVER in a sequential manner results in similar word embedding distributions as doing a combined training on all data at once.
arXiv Detail & Related papers (2022-02-21T10:34:41Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.