関連する実験動画

Updated: Sep 10, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

681

生物医学における命名されたエンティティの認識のための大規模な言語モデルのリソース効率的なインストラクションチューニング

Hui Liu1, Ziyi Chen2, Peilin Li3

  • 1School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, No. 96, JinZhai Road Baohe District, Hefei, 230026, Anhui, China; Suzhou Institute for Advanced Research, University of Science and Technology of China, No. 166, Renai Road, Suzhou, 215123, Jiangsu, China; Department of Precision Machinery and Precision Instruments, University of Science and Technology of China, No. 96, JinZhai Road Baohe District, Hefei, 230026, Anhui, China.

Journal of biomedical informatics
|August 23, 2025
PubMed
まとめ

パラメータ効率的な微調整 (PEFT) は,より少ないリソースで生物医学名付けられたエンティティ認識 (BioNER) のための大規模な言語モデル (LLM) を最適化します. 結果として得られたNERLlama3.1モデルは,完全に微調整されたモデルよりも高い性能と強力な汎用性を得ています.

キーワード:
バイオナーLLM についてマルチタスク BioNERPEFT についてQLoRA (クローラ)自己一貫性 NER プロンプト

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