Levels of Analysis for Large Language Models
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917355051810816 |
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| author | Ku, Alexander Y. Campbell, Declan Bai, Xuechunzi Geng, Jiayi Liu, Ryan Marjieh, Raja McCoy, R. Thomas Nam, Andrew Sucholutsky, Ilia Veselovsky, Veniamin Zhang, Liyi Zhu, Jian-Qiao Griffiths, Thomas L. |
| author_facet | Ku, Alexander Y. Campbell, Declan Bai, Xuechunzi Geng, Jiayi Liu, Ryan Marjieh, Raja McCoy, R. Thomas Nam, Andrew Sucholutsky, Ilia Veselovsky, Veniamin Zhang, Liyi Zhu, Jian-Qiao Griffiths, Thomas L. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_13401 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Levels of Analysis for Large Language Models Ku, Alexander Y. Campbell, Declan Bai, Xuechunzi Geng, Jiayi Liu, Ryan Marjieh, Raja McCoy, R. Thomas Nam, Andrew Sucholutsky, Ilia Veselovsky, Veniamin Zhang, Liyi Zhu, Jian-Qiao Griffiths, Thomas L. Computation and Language Artificial Intelligence 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. |
| title | Levels of Analysis for Large Language Models |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2503.13401 |