让变压器的成功变得有意义
Nicola Angius1, Pietro Perconti1, Alessio Plebe1
1Department of Cognitive Science, University of Messina, Messina, Italy.
Frontiers in artificial intelligence
|April 16, 2025
概括
本研究分析了神经语言模型 (NLM) 如何实现语言能力. 它发现他们的解释策略与认知科学中的解释策略相似,弥合了"机器如何思考"的差距.
科学领域:
- 人工智能的哲学 人工智能的哲学
- 认知科学 认知科学
- 计算语言学 计算语言学
背景情况:
- 人工智能哲学中的核心问题已经从"机器能思考吗?"转变为"机器能思考吗?" 到"机器是如何思考的?" """这是一个很好的例子.
- 神经语言模型 (NLM),特别是那些基于变压器架构的模型,在他们的语言能力方面存在解释性差距.
研究的目的:
- 从认识论上分析目前对NLM语言能力的解释.
- 将NLM的解释策略与认知科学中使用的策略进行比较.
- 调查背景学习背后的机制,并探索共模拟方法.
主要方法:
- 对NLMs现有的解释策略的分析.
- 检查关于思想理论,话语实体跟踪和NLM中属性诱导的实验研究.
- 在认知科学中的功能分析和在语境学习的机械解释 (复制算法,感应头) 的功能分析的审查.
主要成果:
- 对NLM的解释策略与认知科学中的策略相似.
- "复制算法"和"诱导头"为上下文学习提供了机制性的解释.
- 在NLM和大脑活动之间共同模拟的开创性尝试正在出现.
结论:
- 目前对NLM能力的解释与已建立的认知科学方法相一致.
- 目前正在开发对上下文学习的机制性见解.
- 共同模拟为理解人工智能和人类语言处理提供了一种新的方法.
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