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Updated: Jan 17, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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评估在大型语言模型中的语境学习,用于分子性质回归.

Chan Young Joe1, Kyungwoo Song2,3, Rakwoo Chang1

  • 1Department of Applied Chemistry, University of Seoul, Seoul, Republic of Korea.

Journal of computational chemistry
|January 15, 2026
PubMed
概括

大型语言模型 (LLM) 是有前途的,但在科学回归任务的真实上下文学习方面存在困难. 机器学习模型在分子性质预测方面提供了更大的稳定性,特别是在具有挑战性的条件下.

关键词:
在SMILES的代表处.功能性的分布之外的功能.在语境学习中学习.大型语言模型.分子性质预测分子性质预测快捷方式学习学习结构活动 景观指数

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