Under autoregressive and sequential editing, parameter-based knowledge editing methods perform poorly, while the retrieval-based SCR baseline consistently outperforms them across datasets and models.
Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing,
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Benchmarking and Rethinking Knowledge Editing for Large Language Models
Under autoregressive and sequential editing, parameter-based knowledge editing methods perform poorly, while the retrieval-based SCR baseline consistently outperforms them across datasets and models.