The paper first provides background on the architecture of Transformers and the mechanism of knowledge storage in LLMs. It then defines the knowledge editing problem and proposes a new taxonomy to categorize existing knowledge editing methods based on the human learning phases of recognition, association, and mastery.
The recognition phase involves exposing the model to new knowledge within a relevant context, similar to how humans first encounter new information. Methods in this category utilize external memory or retrieval to guide the model's knowledge updates.
The association phase focuses on merging new knowledge representations with the model's existing knowledge, akin to how humans form connections between new and prior concepts. These methods integrate the new knowledge into the model's internal representations, such as the feed-forward neural networks.
The mastery phase aims to have the model fully integrate the knowledge into its parameters, similar to deep human mastery. These methods directly edit the model's weights, either through meta-learning or by locating and editing the specific areas where the knowledge is stored.
The paper then introduces a new benchmark, KnowEdit, which includes six datasets covering a range of knowledge editing tasks, including fact insertion, modification, and erasure. Extensive experiments are conducted to evaluate the performance of representative knowledge editing approaches.
The analysis provides insights into the effectiveness of different knowledge editing methods, the ability to locate and edit specific knowledge within LLMs, and the potential implications of knowledge editing for applications such as efficient machine learning, trustworthy AI, and personalized agents.
Na inny język
z treści źródłowej
arxiv.org
Głębsze pytania