![]() ![]() As a higher-capacity alternative, we propose Semi-Parametric Editing with a Retrieval-Augmented Counterfactual Model (SERAC), which stores edits in an explicit memory and learns to reason over them to modulate the base model's predictions as needed. Existing model editors have shown promise, but also suffer from insufficient expressiveness: they struggle to accurately model an edit's intended scope (examples affected by the edit), leading to inaccurate predictions for test inputs loosely related to the edit, and they often fail altogether after many edits. ![]() ![]() Model editors make local updates to the behavior of base (pre-trained) models to inject updated knowledge or correct undesirable behaviors. Download a PDF of the paper titled Memory-Based Model Editing at Scale, by Eric Mitchell and 4 other authors Download PDF Abstract:Even the largest neural networks make errors, and once-correct predictions can become invalid as the world changes. ![]()
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