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大型语言模型与口音障碍的比较

Takamitsu Watanabe¹, Katsuma Inoue², Yasuo Kuniyoshi²

¹International Research Centre for Neurointelligence, The University of Tokyo Institutes for Advanced Study, 7-3-1 Hongo Bunkyo-ku, Tokyo, 113-0033, Japan.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)

|May 15, 2025

概括

大型语言模型 (LLM) 呈现出类似于感受性失语症的网络动态. 这项研究比较了LLM和非相性大脑活动,建议LLM的新型诊断工具.

科学领域:

计算神经科学是一种计算神经科学.
人工智能的人工智能是人工智能.
神经语言学是一种神经语言学.

背景情况:

大型语言模型 (LLM) 显示流利但往往不准确的响应,反映了人类失言症.
在LLM和无发性大脑的内部信息处理机制仍然在很大程度上未被探索.
行为相似性促使人们对网络动态中的潜在并行进行调查.

研究的目的:

为了比较LLM (ALBERT,GPT-2,Llama-3.1) 的网络动态与人类的非相性大脑活动.
确定能源景观分析是否可以区分类型的失言症和LLM网络状态.
探索这种分析作为LLMs的诊断和改进工具的潜力.

主要方法:

应用能源景观分析来量化LLM和失调大脑中的网络动态.
测量过渡频率 (从一个州到另一个州的移动) 和停留时间 (在一个州度过的时间).
分析了这些指数的频谱,以确定不同的模式.

主要成果:

在LLM中,网络动态显示过渡频率和停留时间的极化分布.
接受性失语表现出两种指数的双模分布,而表达性失语表现出均分布.
过渡频率和停留时间的两极分化准确地分类了感受性失语症,表达性失语症和对照.

结论:

在LLM中,内部信息处理与接受性失言症有相似之处.
能源景观分析为诊断和分类失语提供了一种新的方法.
这种方法提供了一个潜在的工具,通过识别与健康或受损的人类认知的处理相似性来提高LLM的性能.

关键词:

亚法西亚 (aphasia) 是一种失言症.能源景观分析能源景观分析大型语言模型

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