Mass-Editing Memory in a TransformerDownload PDF

Published: 01 Feb 2023, 19:19, Last Modified: 02 Mar 2023, 06:59ICLR 2023 notable top 25%Readers: Everyone
Keywords: language models, GPT, transformers, model editing, factual associations, memory
TL;DR: An algorithm that can make tens of thousands of edits to an autoregressive transformer's memory.
Abstract: Recent work has shown exciting promise in updating large language models with new memories, so as to replace obsolete information or add specialized knowledge. However, this line of work is predominantly limited to updating single associations. We develop MEMIT, a method for directly updating a language model with many memories, demonstrating experimentally that it can scale up to thousands of associations for GPT-J (6B) and GPT-NeoX (20B), exceeding prior work by an order of magnitude. Our code and data will be open-sourced upon publication.
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