Papers by Daniel Mela

1 papers
Mass-Editing Memory with Attention in Transformers: A cross-lingual exploration of knowledge (2024.findings-acl)

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Challenge: Recent studies have explored methods for updating and modifying factual knowledge in large language models, often focusing on specific multi-layer perceptron blocks.
Approach: They propose a method that allows users to edit factual associations without catastrophic forgetting.
Outcome: The proposed method achieves 10% increase in magnitude metrics while requiring minimal parameter modifications.

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