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arXiv · 2307.03637

Discovering Variable Binding Circuitry with Desiderata

Xander Davies·Max Nadeau·Nikhil Prakash·Tamar Rott Shaham·David Bau
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Abstract

Recent work has shown that computation in language models may be human-understandable, with successful efforts to localize and intervene on both single-unit features and input-output circuits. Here, we introduce an approach which extends causal mediation experiments to automatically identify model components responsible for performing a specific subtask by solely specifying a set of \textit{desiderata}, or causal attributes of the model components executing that subtask. As a proof of concept, we apply our method to automatically discover shared \textit{variable binding circuitry} in LLaMA-13B, which retrieves variable values for multiple arithmetic tasks. Our method successfully localizes variable binding to only 9 attention heads (of the 1.6k) and one MLP in the final token's residual stream.

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Xander Davies, Max Nadeau, Nikhil Prakash, Tamar Rott Shaham, David Bau. 2023-07-07. Discovering Variable Binding Circuitry with Desiderata. https://arxiv.org/abs/2307.03637

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