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相关概念视频

Language and Cognition01:27

Language and Cognition

340
Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
340

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相关实验视频

Updated: Jun 19, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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使用大型语言模型进行自动抽象选的问答框架.

Opeoluwa Akinseloyin1, Xiaorui Jiang2, Vasile Palade1

  • 1Centre for Computational Science and Mathematical Modelling, Coventry University, Coventry CV1 2TT, United Kingdom.

Journal of the American Medical Informatics Association : JAMIA
|July 23, 2024
PubMed
概括
此摘要是机器生成的。

大型语言模型 (LLM) 通过使用问答框架来增强系统审查 (SR) 抽象选. 这种方法有效地优先研究,比传统方法提高效率.

关键词:
抽象的选抽象的选自动化系统性审查大型语言模型问题 回答 回答 问题 回答零射击重新排名重新排名

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科学领域:

  • 医疗信息学 医疗信息学
  • 医疗保健中的人工智能
  • 证据综合 证据综合

背景情况:

  • 系统审查 (SRs) 需要严格的抽象选,这个过程往往耗时且资源密集.
  • 目前抽象查的方法可能是低效的,导致证据合成的延迟.

研究的目的:

  • 开发和验证一种新的框架,用于在SR中进行抽象选,使用大型语言模型 (LLM) 的零射击能力.
  • 将抽象选转化为问答 (QA) 任务,利用LLM使摘要与SR选择标准保持一致.

主要方法:

  • 通过将抽象选作为质量保证任务,LLM被用来优先考虑候选人研究,其中选择标准充当问题.
  • 该框架涉及将标准分解为问题,提示LLM,评分答案,并结合包含/排除决策的答案.
  • 验证是在CLEF eHealth 2019 Task 2基准上进行的,使用GPT-3.5并与31个数据集的传统和微调的BERT家族模型进行比较.

主要成果:

  • 拟议的基于LLM的质量保证框架在优先考虑研究方面,比传统的信息检索和微调的BERT模型具有显著的优势.
  • 通过根据摘要和选择标准之间的语义对齐重新排名LLM答案,实现了性能改善.
  • 该框架在各种SR类别中表现出一致的有效性,并证明在不同的LLM中具有可行性.

结论:

  • 通过使用开发的质量保证框架,LLM在优先考虑候选研究方面非常有效,以便在SR中进行抽象选.
  • 利用选择标准作为查询显著提高了自动抽象选的性能.
  • 该研究强调了LLM在简化和提高证据综合过程效率方面的潜力.