ARETE: an R package for Automated REtrieval from TExt with large language models

• URL: http://arxiv.org/abs/2511.04573v1
• Date: Thu, 06 Nov 2025 17:26:48 GMT
• Title: ARETE: an R package for Automated REtrieval from TExt with large language models
• Authors: Vasco V. Branco, Jandó Benedek, Lidia Pivovarova, Luís Correia, Pedro Cardoso,
• Abstract summary: A machine-to-machine approach to extracting species occurrences from occurrence data is presented.<n>We demonstrate the usefulness of the approach by comparing range maps produced using GBIF data and with those automatically extracted for 100 species.<n>Newly extracted data allowed expand the known Extent Occurrence by a mean of magnitude, revealing new areas where outlier species were found in the past.
• Score: 0.5541644538483946
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 1. A hard stop for the implementation of rigorous conservation initiatives is our lack of key species data, especially occurrence data. Furthermore, researchers have to contend with an accelerated speed at which new information must be collected and processed due to anthropogenic activity. Publications ranging from scientific papers to gray literature contain this crucial information but their data are often not machine-readable, requiring extensive human work to be retrieved. 2. We present the ARETE R package, an open-source software aiming to automate data extraction of species occurrences powered by large language models, namely using the chatGPT Application Programming Interface. This R package integrates all steps of the data extraction and validation process, from Optical Character Recognition to detection of outliers and output in tabular format. Furthermore, we validate ARETE through systematic comparison between what is modelled and the work of human annotators. 3. We demonstrate the usefulness of the approach by comparing range maps produced using GBIF data and with those automatically extracted for 100 species of spiders. Newly extracted data allowed to expand the known Extent of Occurrence by a mean three orders of magnitude, revealing new areas where the species were found in the past, which mayhave important implications for spatial conservation planning and extinction risk assessments. 4. ARETE allows faster access to hitherto untapped occurrence data, a potential game changer in projects requiring such data. Researchers will be able to better prioritize resources, manually verifying selected species while maintaining automated extraction for the majority. This workflow also allows predicting available bibliographic data during project planning.

Related papers

• External Data Extraction Attacks against Retrieval-Augmented Large Language Models [70.47869786522782]
  RAG has emerged as a key paradigm for enhancing large language models (LLMs)<n>RAG introduces new risks of external data extraction attacks (EDEAs), where sensitive or copyrighted data in its knowledge base may be extracted verbatim.<n>We present the first comprehensive study to formalize EDEAs against retrieval-augmented LLMs.
  arXiv  Detail & Related papers  (2025-10-03T12:53:45Z)
• Large Language Models and Synthetic Data for Monitoring Dataset Mentions in Research Papers [0.0]
  This paper presents a machine learning framework that automates dataset mention detection across research domains.<n>We employ zero-shot extraction from research papers, an LLM-as-a-Judge for quality assessment, and a reasoning agent for refinement to generate a weakly supervised synthetic dataset.<n>At inference, a ModernBERT-based classifier efficiently filters dataset mentions, reducing computational overhead while maintaining high recall.
  arXiv  Detail & Related papers  (2025-02-14T16:16:02Z)
• Dynamic Data Pruning for Automatic Speech Recognition [58.95758272440217]
  We introduce Dynamic Data Pruning for ASR (DDP-ASR), which offers fine-grained pruning granularities specifically tailored for speech-related datasets. Our experiments show that DDP-ASR can save up to 1.6x training time with negligible performance loss.
  arXiv  Detail & Related papers  (2024-06-26T14:17:36Z)
• Using Large Language Models to Enrich the Documentation of Datasets for Machine Learning [1.8270184406083445]
  We explore using large language models (LLM) and prompting strategies to automatically extract dimensions from documents. Our approach could aid data publishers and practitioners in creating machine-readable documentation. We have released an open-source tool implementing our approach and a replication package, including the experiments' code and results.
  arXiv  Detail & Related papers  (2024-04-04T10:09:28Z)
• DataAgent: Evaluating Large Language Models' Ability to Answer Zero-Shot, Natural Language Queries [0.0]
  We evaluate OpenAI's GPT-3.5 as a "Language Data Scientist" (LDS) The model was tested on a diverse set of benchmark datasets to evaluate its performance across multiple standards.
  arXiv  Detail & Related papers  (2024-03-29T22:59:34Z)
• A Survey on Data Selection for Language Models [148.300726396877]
  Data selection methods aim to determine which data points to include in a training dataset. Deep learning is mostly driven by empirical evidence and experimentation on large-scale data is expensive. Few organizations have the resources for extensive data selection research.
  arXiv  Detail & Related papers  (2024-02-26T18:54:35Z)
• Zero-shot Retrieval: Augmenting Pre-trained Models with Search Engines [83.65380507372483]
  Large pre-trained models can dramatically reduce the amount of task-specific data required to solve a problem, but they often fail to capture domain-specific nuances out of the box. This paper shows how to leverage recent advances in NLP and multi-modal learning to augment a pre-trained model with search engine retrieval.
  arXiv  Detail & Related papers  (2023-11-29T05:33:28Z)
• STAR: Boosting Low-Resource Information Extraction by Structure-to-Text Data Generation with Large Language Models [56.27786433792638]
  STAR is a data generation method that leverages Large Language Models (LLMs) to synthesize data instances. We design fine-grained step-by-step instructions to obtain the initial data instances. Our experiments show that the data generated by STAR significantly improve the performance of low-resource event extraction and relation extraction tasks.
  arXiv  Detail & Related papers  (2023-05-24T12:15:19Z)
• pyBKT: An Accessible Python Library of Bayesian Knowledge Tracing Models [0.0]
  We introduce pyBKT, a library of model extensions for knowledge tracing. The library provides data generation, fitting, prediction, and cross-validation routines. pyBKT is open source and open license for the purpose of making knowledge tracing more accessible to communities of research and practice.
  arXiv  Detail & Related papers  (2021-05-02T03:08:53Z)
• Zero-shot Slot Filling with DPR and RAG [10.577238010892287]
  The ability to automatically extract Knowledge Graphs (KG) from a given collection of documents is a long-standing problem in Artificial Intelligence. Recent advancements in the field try to solve this task in an end-to-end fashion using retrieval-based language models. In this paper, we describe several strategies we adopted to improve the retriever and the generator of RAG in order to make it a better slot filler.
  arXiv  Detail & Related papers  (2021-04-17T18:24:51Z)
• PyODDS: An End-to-end Outlier Detection System with Automated Machine Learning [55.32009000204512]
  We present PyODDS, an automated end-to-end Python system for Outlier Detection with Database Support. Specifically, we define the search space in the outlier detection pipeline, and produce a search strategy within the given search space. It also provides unified interfaces and visualizations for users with or without data science or machine learning background.
  arXiv  Detail & Related papers  (2020-03-12T03:30:30Z)

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.