Large language models (LLMs) require frequent knowledge updates to reflect changing facts and mitigate hallucinations.To meet this demand, lifelong knowledge editing has emerged as a continual approach to modify specific pieces of knowledge without retraining the entire model.Existing parameter-editing methods struggle with stability during sequential edits due to catastrophic forgetting.While retrieval-based approaches are proposed to alleviate this issue, their applicability remains limited across various datasets because of high training costs.To address these limitations and enhance scalability in lifelong settings, we propose LightEdit.Our framework first selects relevant knowledge from retrieved information to modify the query effectively.It then incorporates a decoding strategy to suppress the model's original knowledge probabilities, thereby enabling efficient edits based on the selected information.Extensive experiments on ZSRE, Counterfact, and RIPE benchmarks demonstrate that LightEdit outperforms existing lifelong knowledge editing methods.Furthermore, by minimizing training costs, LightEdit achieves cost-effective scalability, enabling easy adaptation to various datasets.1

Dahyun Jung, Jaehyung Seo, Jaewook Lee, Chanjun Park, Heuiseok Lim. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Korean morphological variations present unique opportunities and challenges in natural language processing (NLP), necessitating an advanced understanding of morpheme-based sentence construction.The complexity of morphological variations allows for diverse sentence forms based on the syntactic-semantic integration of functional morphemes (i.e., affixes) to lexical morphemes (i.e., roots).With this in mind, we propose a method -CHEF, replicating the morphological transformations inherent in sentences based on lexical and functional morpheme combinations through generative data augmentation.CHEF operates using a morpheme blender and a label discriminator, thereby enhancing the diversity of Korean sentence forms by capturing the properties of agglutination while maintaining label consistency.We conduct experiments on Korean multiple classification datasets, improving model performance in full-and few-shot settings.Our proposed method boosts performance beyond the preceding data augmentation methods without incurring external data usage.We demonstrate that our approach achieves comparable results yielded by augmentation techniques that use large language models (LLMs).