Knowledge editing emerges as a promising approach for updating target knowledge in Large Language Models (LLMs) in a timely manner, thereby preventing undesirable behaviors stemming from outdated, inaccurate, or incomplete knowledge. However, existing methods mainly focus on instance-level editing, which is prone to over-editing risk featuring knowledge degradation and general ability deterioration, due to redundant instance-specific modifications for knowledge. To mitigate the over-editing risk, we explore the rule-level editing problem that avoids case-by-case modification by generalizing rule-level knowledge to update rule-derived instances. We further construct a benchmark called RuleEdit for systematic evaluation on rule-level editing. Moreover, we propose a Rule-Transfer Editing (RTE) method to facilitate effective updates and generalizations of rule-level knowledge in LLMs. Experimental results highlight our significant improvements, with the enhancements of 28.1% in portability and 8.1% in average performance over the best-performing baselines for LLaMA-2-7B on RULE_mix.

Execution failures are common in daily life when individuals perform procedural tasks, such as cooking or handicrafts making. Retrieving relevant procedural documents that align closely with both the content of steps and the overall execution sequence can help correct these failures with fewer modifications. However, existing retrieval methods, which primarily focus on declarative knowledge, often neglect the execution sequence structures inherent in procedural documents. To tackle this challenge, we introduce a new dataset Procedural Questions, and propose a retrieval model Graph-Fusion Procedural Document Retriever (GFPDR) which integrates procedural graphs with document representations. Extensive experiments demonstrate the effectiveness of GFPDR, highlighting its superior performance in procedural document retrieval compared to existing models.