Mass-Editing Memory with Attention in Transformers: A cross-lingual exploration of knowledge
This work addresses the challenge of efficiently editing knowledge in multilingual models, though it appears incremental as it builds on existing editing methods.
The paper tackles the problem of updating factual knowledge in large language models across languages by proposing MEMAT, a method that uses attention mechanisms to achieve a 10% increase in magnitude metrics and benefits untrained languages with minimal parameter changes.
Recent research has explored methods for updating and modifying factual knowledge in large language models, often focusing on specific multi-layer perceptron blocks. This study expands on this work by examining the effectiveness of existing knowledge editing methods across languages and delving into the role of attention mechanisms in this process. Drawing from the insights gained, we propose Mass-Editing Memory with Attention in Transformers (MEMAT), a method that achieves significant improvements in all metrics while requiring minimal parameter modifications. MEMAT delivers a remarkable 10% increase in magnitude metrics, benefits languages not included in the training data and also demonstrates a high degree of portability. Our code and data are at https://github.com/dtamayo-nlp/MEMAT.