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Updated: Sep 9, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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命令データマイニングの対抗性プロンプトのLLMの堅強さにより注意を払う

Qiang Wang1, Dawei Feng1, Xu Zhang1

  • 1National Key Laboratory of Parallel and Distributed Computing, College of Computer Science and Technology, National University of Defense Technology, Hunan Changsha, 410073, China.

Neural networks : the official journal of the International Neural Network Society
|September 3, 2025
PubMed
まとめ

この研究は,高品質の指示データを採掘することによって,大規模な言語モデル (LLM) を改善するための新しい方法を導入します. 挑戦的な指示サンプルを特定する技術を開発し 敵対的なプロンプトに対するLLMの強さを高めました

キーワード:
対立命令の出力エンベディングの一貫性逆の指示に伴う困難命令データマイニング指示のチューニングLLM 堅固さ

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