Toward Total Recall: Enhancing FAIRness through AI-Driven Metadata Standardization

• URL: http://arxiv.org/abs/2504.05307v2
• Date: Sat, 07 Jun 2025 23:07:07 GMT
• Title: Toward Total Recall: Enhancing FAIRness through AI-Driven Metadata Standardization
• Authors: Sowmya S Sundaram, Rafael S. Gonçalves, Mark A Musen,
• Abstract summary: We present a method that combines GPT-4 with structured metadata templates from the CEDAR knowledge base to automatically standardize metadata.<n>Our standardization process involves using CEDAR templates to guide GPT-4 in accurately correcting and refining metadata entries in bulk.
• Score: 2.4347641401231126
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Scientific metadata often suffer from incompleteness, inconsistency, and formatting errors, which hinder effective discovery and reuse of the associated datasets. We present a method that combines GPT-4 with structured metadata templates from the CEDAR knowledge base to automatically standardize metadata and to ensure compliance with established standards. A CEDAR template specifies the expected fields of a metadata submission and their permissible values. Our standardization process involves using CEDAR templates to guide GPT-4 in accurately correcting and refining metadata entries in bulk, resulting in significant improvements in metadata retrieval performance, especially in recall -- the proportion of relevant datasets retrieved from the total relevant datasets available. Using the BioSample and GEO repositories maintained by the National Center for Biotechnology Information (NCBI), we demonstrate that retrieval of datasets whose metadata are altered by GPT-4 when provided with CEDAR templates (GPT-4+CEDAR) is substantially better than retrieval of datasets whose metadata are in their original state and that of datasets whose metadata are altered using GPT-4 with only data-dictionary guidance (GPT-4+DD). The average recall increases dramatically, from 17.65\% with baseline raw metadata to 62.87\% with GPT-4+CEDAR. Furthermore, we evaluate the robustness of our approach by comparing GPT-4 against other large language models, including LLaMA-3 and MedLLaMA2, demonstrating consistent performance advantages for GPT-4+CEDAR. These results underscore the transformative potential of combining advanced language models with symbolic models of standardized metadata structures for more effective and reliable data retrieval, thus accelerating scientific discoveries and data-driven research.

Related papers

• Detection of Personal Data in Structured Datasets Using a Large Language Model [0.0]
  We propose a novel approach for detecting personal data in structured datasets, leveraging GPT-4o.<n>We compare our approach to alternative methods, including Microsoft Presidio and CASSED, evaluating them on multiple datasets.
  arXiv  Detail & Related papers  (2025-06-27T15:16:43Z)
• Instructify: Demystifying Metadata to Visual Instruction Tuning Data Conversion [41.10541692094663]
  We propose an open and unified recipe and approach for converting available metadata to VisIT instructions using open LLMs.<n>Our approach can reproduce or enhance the data quality of available VisIT datasets when applied to the same image data and metadata sources.
  arXiv  Detail & Related papers  (2025-05-23T17:14:12Z)
• Metadata-based Data Exploration with Retrieval-Augmented Generation for Large Language Models [3.7685718201378746]
  This research introduces a new architecture for data exploration which employs a form of Retrieval-Augmented Generation (RAG) to enhance metadata-based data discovery. The proposed framework offers a new method for evaluating semantic similarity among heterogeneous data sources.
  arXiv  Detail & Related papers  (2024-10-05T17:11:37Z)
• RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs [60.38044044203333]
  Large language models (LLMs) typically utilize the top-k contexts from a retriever in retrieval-augmented generation (RAG) We propose a novel instruction fine-tuning framework RankRAG, which instruction-tunes a single LLM for the dual purpose of context ranking and answer generation in RAG. For generation, we compare our model with many strong baselines, including GPT-4-0613, GPT-4-turbo-2024-0409, and ChatQA-1.5, an open-sourced model with the state-of-the-art performance on RAG benchmarks.
  arXiv  Detail & Related papers  (2024-07-02T17:59:17Z)
• Dataset Regeneration for Sequential Recommendation [69.93516846106701]
  We propose a data-centric paradigm for developing an ideal training dataset using a model-agnostic dataset regeneration framework called DR4SR. To demonstrate the effectiveness of the data-centric paradigm, we integrate our framework with various model-centric methods and observe significant performance improvements across four widely adopted datasets.
  arXiv  Detail & Related papers  (2024-05-28T03:45:34Z)
• Use of a Structured Knowledge Base Enhances Metadata Curation by Large Language Models [2.186740861187042]
  Metadata play a crucial role in ensuring the findability, accessibility, interoperability, and reusability of datasets.<n>This paper investigates the potential of large language models (LLMs) to improve adherence to metadata standards.<n>We conducted experiments on 200 random data records describing human samples relating to lung cancer from the NCBI BioSample repository.
  arXiv  Detail & Related papers  (2024-04-08T22:29:53Z)
• Genixer: Empowering Multimodal Large Language Models as a Powerful Data Generator [63.762209407570715]
  Genixer is a comprehensive data generation pipeline consisting of four key steps. A synthetic VQA-like dataset trained with LLaVA1.5 enhances performance on 10 out of 12 multimodal benchmarks. MLLMs trained with task-specific datasets can surpass GPT-4V in generating complex instruction tuning data.
  arXiv  Detail & Related papers  (2023-12-11T09:44:41Z)
• TarGEN: Targeted Data Generation with Large Language Models [51.87504111286201]
  TarGEN is a multi-step prompting strategy for generating high-quality synthetic datasets. We augment TarGEN with a method known as self-correction empowering LLMs to rectify inaccurately labeled instances. A comprehensive analysis of the synthetic dataset compared to the original dataset reveals similar or higher levels of dataset complexity and diversity.
  arXiv  Detail & Related papers  (2023-10-27T03:32:17Z)
• Using GPT-4 to Augment Unbalanced Data for Automatic Scoring [0.5586073503694489]
  We introduce a novel text data augmentation framework leveraging GPT-4, a generative large language model. We crafted prompts for GPT-4 to generate responses, especially for minority scoring classes. We finetuned DistillBERT for automatic scoring based on the augmented and original datasets.
  arXiv  Detail & Related papers  (2023-10-25T01:07:50Z)
• ExtractGPT: Exploring the Potential of Large Language Models for Product Attribute Value Extraction [52.14681890859275]
  E-commerce platforms require structured product data in the form of attribute-value pairs. BERT-based extraction methods require large amounts of task-specific training data. This paper explores using large language models (LLMs) as a more training-data efficient and robust alternative.
  arXiv  Detail & Related papers  (2023-10-19T07:39:00Z)
• Instruction Tuning with GPT-4 [107.55078894215798]
  We present the first attempt to use GPT-4 to generate instruction-following data for finetuning large language models. Our early experiments on instruction-tuned LLaMA models show that the 52K English and Chinese instruction-following data generated by GPT-4 leads to superior zero-shot performance on new tasks.
  arXiv  Detail & Related papers  (2023-04-06T17:58:09Z)

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.