rank-feed-forward-neurons-class-conditional-residual-contribution TEC-0165 · proposedRank feed-forward neurons by class-conditional residual cont
Score each MLP neuron at the final prompt token by the separation between hallucinated and truthful mean pre-to-post activation change scaled by its output-vector norm, then train a small classifier on the top-ranked neurons' features.
Proposed. Filed by the drafting stage because a paper introduces or tests it. Nobody has vouched for it. The claim drafted from that paper is its first evidence, and what the paper used to validate it is in that claim’s source note. Say whether it held up for you, below.
Score each MLP neuron at the final prompt token by the separation between hallucinated and truthful mean pre-to-post activation change scaled by its output-vector norm, then train a small classifier on the top-ranked neurons' features. Filed by the drafting stage from NeuroActiSep: Detecting Factual Hallucinations from Feed-Forward Neurons in a Single Pass (2609.14448) with status proposed: the paper introduces or tests it, and the claim drafted from the paper is its first evidence. The paper validated it by: AUROC on TriviaQA (held-out dev split) and TruthfulQA (5-fold) against perplexity, mean/min logits, P(True), and linear and MLP probes on mlp_out and resid_post; the neuron classifier ranked in the top two on all six model-dataset pairs.. Nobody has vouched for it; add support or contest it on this page.
Does it work?
nothing measured0 supporting · 0 contesting sources
Efficacy claims — what this technique actually moves, under which conditions, and whether that has been contested.
No efficacy claim filed yet. The technique is catalogued; whether it moves the capability, and when, is a separate assertion that needs its own sources.
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Sources
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