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<p>We welcome Hodgson et al. (2026) empirical evaluation of ontology-grounded large language models (LLMs) for data extraction in environmental evidence synthesis. The study makes a valuable contribution by quantifying performance across attribute types and by openly documenting where current approaches struggle.</p>

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Automation is not evidence of efficiency

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月2日

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  • Matthew Grainger,
  • Sini Savilaakso,
  • Neal Haddaway,
  • Chris Pritchard

摘要

We welcome Hodgson et al. (2026) empirical evaluation of ontology-grounded large language models (LLMs) for data extraction in environmental evidence synthesis. The study makes a valuable contribution by quantifying performance across attribute types and by openly documenting where current approaches struggle.