Are Arithmetic Heuristic Neurons Form-Invariant? A Mechanistic Analysis of Symbols, Text, and Code in LLMs
A new preprint investigates whether the neurons responsible for arithmetic in large language models (LLMs) are form-invariant across symbolic arithmetic, natural language word problems, and Python code. Using attribution and activation patching on Llama-3 models, the researchers identified a compact set of neurons shared across all three formats, which are both necessary and sufficient for late-layer arithmetic computation. Transferring activations from successful to failed executions recovered over 97% of incorrect predictions for addition and subtraction, suggesting that cross-format failures are due to activation states rather than distinct neural circuits.
Why it matters: This work provides mechanistic evidence that LLMs use a shared neural circuit for arithmetic across different input formats, informing future efforts to improve model robustness and interpretability.
Full story at: arXiv Computation and Language ↗