Leveraging Logical Rules in Knowledge Editing: A Cherry on the Top
<|reference_start|>Leveraging Logical Rules in Knowledge Editing: A Cherry on the Top: Multi-hop Question Answering (MQA) under knowledge editing (KE) is a key challenge in Large Language Models (LLMs). While best-performing solutions in this domain use a plan and solve paradigm to split a question into sub-questions followed by response generation, we claim that this approach is sub-optimal as it fails for hard to decompose questions, and it does not explicitly cater to correlated knowledge updates resulting as a consequence of knowledge edits. This has a detrimental impact on the overall consistency of the updated knowledge. To address these issues, in this paper, we propose a novel framework named RULE-KE, i.e., RULE based Knowledge Editing, which is a cherry on the top for augmenting the performance of all existing MQA methods under KE. Specifically, RULE-KE leverages rule discovery to discover a set of logical rules. Then, it uses these discovered rules to update knowledge about facts highly correlated with the edit. Experimental evaluation using existing and newly curated datasets (i.e., RKE-EVAL) shows that RULE-KE helps augment both performances of parameter-based and memory-based solutions up to 92% and 112.9%, respectively.<|reference_end|>
@article{cheng2024leveraging,
    title={Leveraging Logical Rules in Knowledge Editing: A Cherry on the Top},
    author={Keyuan Cheng, Muhammad Asif Ali, Shu Yang, Gang Lin, Yuxuan Zhai,
  Haoyang Fei, Ke Xu, Lu Yu, Lijie Hu, and Di Wang},
    journal={arXiv preprint arXiv:2405.15452},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.15452},
    primaryClass={cs.CL cs.AI cs.LG}
}