What is recorded here?
Learns small shared editor networks on factorized per-example gradients, producing targeted weight changes while training against edit-neighborhood and locality losses without modifying the base model before an edit.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Requires editor training and chosen editable layers, edit/locality distributions and objectives. Locality is measured on sampled unrelated inputs, not guaranteed everywhere. O(d) editor parameterization is not an all-resource constant-cost claim.
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People and systems
Eric Mitchell; Charles Lin; Antoine Bosselut; Chelsea Finn; Christopher D. Manning
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
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English catalogue narrative
MEND: low-rank gradient-transform networks for local model editing
Learns small shared editor networks on factorized per-example gradients, producing targeted weight changes while training against edit-neighborhood and locality losses without modifying the base model before an edit.
Scientific scope
Requires editor training and chosen editable layers, edit/locality distributions and objectives. Locality is measured on sampled unrelated inputs, not guaranteed everywhere. O(d) editor parameterization is not an all-resource constant-cost claim.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Eric Mitchell; Charles Lin; Antoine Bosselut; Chelsea Finn; Christopher D. Manning
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.