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The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks

Ziqian Zhong · Ziming Liu · Max Tegmark · Jacob Andreas

Hall C2 (level 1 gate 9 south of food court)
[ ] [ Livestream: Visit Oral 6A LLMs ]
Thu 14 Dec 1:50 p.m. — 2:05 p.m. PST


Do neural networks, trained on well-understood algorithmic tasks, reliably rediscover known algorithms? Several recent studies, on tasks ranging from group operations to in-context linear regression, have suggested that the answer is yes. Using modular addition as a prototypical problem, we show that algorithm discovery in neural networks is sometimes more complex: small changes to model hyperparameters and initializations can induce discovery of qualitatively different algorithms from a fixed training set, and even learning of multiple different solutions in parallel. In modular addition, we specifically show that models learn a known Clock algorithm, a previously undescribed, less intuitive, but comprehensible procedure we term the Pizza algorithm, and a variety of even more complex procedures. Our results show that even simple learning problems can admit a surprising diversity of solutions, motivating the development of new tools for mechanistically characterizing the behavior of neural networks across the algorithmic phase space.

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