用认知科学的分析层次，解析大模型的内部运作机制。
Levels of Analysis for Large Language Models
- 借鉴马尔的认知层次框架，分层解析大模型信息处理
- 提出可复用的方法论，提升大模型可解释性
- 适合研究大模型机理与可解释性的研究人员
现代人工智能系统（如大语言模型）日益强大，但理解难度也不断上升。我们认识到这一问题与历史上理解人类心智的困难类似，因此认为认知科学中发展出的方法对理解大语言模型具有参考价值。本文基于大卫·马尔提出的层次分析框架，重新审视适用于每一层次的认知科学方法，并展示它们在揭示大语言模型行为特征与内部结构方面的潜力。目标是为理解这类新型智能系统提供一套实用的分析工具。
原文摘要 · Abstract (English)
Modern artificial intelligence systems, such as large language models, are increasingly powerful but also increasingly hard to understand. Recognizing this problem as analogous to the historical difficulties in understanding the human mind, we argue that methods developed in cognitive science can be useful for understanding large language models. We propose a framework for applying these methods based on the levels of analysis that David Marr proposed for studying information processing systems. By revisiting established cognitive science techniques relevant to each level and illustrating their potential to yield insights into the behavior and internal organization of large language models, we aim to provide a toolkit for making sense of these new kinds of minds.
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