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Updated: Feb 12, 2026

Empirical, Metagenomic, and Computational Techniques Illuminate the Mechanisms by which Fungicides Compromise Bee Health
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Empirical, Metagenomic, and Computational Techniques Illuminate the Mechanisms by which Fungicides Compromise Bee Health

Published on: October 9, 2017

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エンティティと関係抽出のためのハードプロンプトされたLLMにおける一貫性-正確性相関:植物保健データからの経験的発見

Xinzhi Yao1,2, Claire Nédellec3, Jingbo Xia2

  • 1National Research Institute for Agriculture, Food and Environment, University Paris-Saclay, MaIAGE, Jouy-en-Josas, France.

Genomics & informatics
|February 11, 2026
PubMed
まとめ

大型言語モデル (LLM) は,情報抽出 (IE) の正確性と一貫性が異なることを示しています. 出力安定性を測定する一貫性は,重要な信頼性指標であり,特に表面的な変動よりも意味論的な意味を考えるときに重要です.

キーワード:
精度 精度 精度 精度一貫性 一貫性とは文書レベルの情報抽出 文書レベルの情報抽出大型言語モデル評価植物保健 植物保健についてプロンプティング (Prompting) とはリレーショナル情報抽出 リレーショナル情報抽出

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