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相关概念视频

Higher Mental Functions of the Brain: Language01:10

Higher Mental Functions of the Brain: Language

618
Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
Language formation and comprehension take place in the dominant hemisphere. The dominant hemisphere is responsible for understanding the meaning of spoken, written, or sign language, as well as the ability to communicate. For most people, the left hemisphere is the dominant one. The right hemisphere, then, gives tone and emotional context to the...
618
Language and Cognition01:27

Language and Cognition

291
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.
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相关实验视频

Updated: May 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

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通过问LLM问题,为语言神经科学制作可解释的嵌入式.

Vinamra Benara1, Chandan Singh2, John X Morris3

  • 1UC Berkeley.

Advances in neural information processing systems
|April 25, 2025
PubMed
概括

我们介绍了可解释机器学习的问答嵌入式 (QA-Emb). QA-Emb使用是/否问题来创建可理解的文本嵌入,在神经科学应用中表现优于基线.

科学领域:

  • 神经科学是一个神经科学.
  • 人工智能的人工智能
  • 计算语言学 计算语言学

背景情况:

  • 大型语言模型 (LLM) 提供了先进的文本嵌入,但缺乏可解释性,这对于神经科学等科学应用至关重要.
  • 在科学中越来越多地使用LLM,因此需要方法来理解它们的内部表示.

研究的目的:

  • 调查是否可以使用提示从LLM生成可解释的嵌入.
  • 引入和评估一种用于创建可解释嵌入式的新方法,称为问答嵌入式 (QA-Emb).

主要方法:

  • 开发了QA-Emb,其中每个特征对应于LLM对特定问题的对/否答案.
  • 通过选择相关问题而不是优化模型重量来训练QA-Emb.
  • 应用QA-Emb来预测功能磁共振成像 (fMRI) 语音对语言刺激的反应.

主要成果:

  • 在预测fMRI反应方面,QA-Emb显著优于已知的可解释基线.
  • 该方法在最少的问题集中实现了高性能.
  • QA-Emb模型可以通过更简单的模型高效地近似.

结论:

  • QA-Emb提供了一种灵活和可解释的方法,用于生成语义大脑表示的特征空间.

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  • 这种方法有助于具体化和评估我们对大脑如何处理语言的理解.
  • 在自然语言处理 (NLP) 任务中,QA-Emb对更广泛的应用有希望.