Finding the Heads and the Neurons Responsible for Network Information Retrieval in Language Models
Read the original on arXiv Machine Learning →The study investigates whether particular attention heads and individual neurons within those heads in language models are responsible for detecting network infrastructure information—specifically hostnames paired with IP addresses. Using causal ablation and selective testing across five models from three architecture families, the authors find that a small subset of heads reliably identifies such information with near-perfect accuracy. However, the extent to which this responsibility is concentrated in a single neuron varies by model; in some cases a single neuron suffices, while in others the signal is distributed across the head. The findings generalize to an independent reverse‑DNS dataset, though single‑neuron detectors are less robust.
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