Retrieval-augmented Multilingual Knowledge Editing
Knowledge represented in Large Language Models (LLMs) is quite often incorrect and can also become obsolete over time. Updating knowledge via fine-tuning is computationally resource-hungry and not reliable, and so knowledge editing (KE) has developed as an effective and economical alternative to inject new knowledge or to fix factual errors in LLMs. Although there has been considerable interest in this area, current KE research exclusively focuses on the monolingual setting, typically in English. However, what happens if the new knowledge is supplied in one language, but we would like to query the LLM in a different language? To address the problem of multilingual knowledge editing, we propose Retrieval-augmented Multilingual Knowledge Editor (ReMaKE) to update new knowledge in LLMs. ReMaKE can perform model-agnostic knowledge editing in multilingual settings. ReMaKE concatenates the new knowledge retrieved from a multilingual knowledge base with prompts. Our experimental results show that ReMaKE outperforms baseline knowledge editing methods by a significant margin and is the first KE method to work in a multilingual setting. We provide our multilingual knowledge editing dataset (MzsRE) in 12 languages, which along with code, and additional project information is available at https://github.com/Vicky-Wil/ReMaKE.
Code (3)
Tasks
knowledge editingRetrievalMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
CroSearch-R1: Better Leveraging Cross-lingual Knowledge for Retrieval-Augmented Generation
A multilingual collection may contain useful knowledge in other languages to supplement and correct the facts in the original language for Retrieval-Augmented Generation (RAG). However, the vanilla approach that simply c…
Reinforcement LearningLifelong Knowledge Editing for LLMs with Retrieval-Augmented Continuous Prompt Learning
Model editing aims to correct outdated or erroneous knowledge in large language models (LLMs) without the need for costly retraining. Lifelong model editing is the most challenging task that caters to the continuous edit…
knowledge editingLifelong learningModel EditingPrompt Learning+1Language-Coupled Reinforcement Learning for Multilingual Retrieval-Augmented Generation
Multilingual retrieval-augmented generation (MRAG) requires models to effectively acquire and integrate beneficial external knowledge from multilingual collections. However, most existing studies employ a unitive process…
Reinforcement LearningNot All Languages are Equal: Insights into Multilingual Retrieval-Augmented Generation
RALMs (Retrieval-Augmented Language Models) broaden their knowledge scope by incorporating external textual resources. However, the multilingual nature of global knowledge necessitates RALMs to handle diverse languages, …
AllRetrievalRetrieval-augmented GenerationSelection bias+1Multilingual Retrieval-Augmented Generation for Knowledge-Intensive Task
Retrieval-augmented generation (RAG) has become a cornerstone of contemporary NLP, enhancing large language models (LLMs) by allowing them to access richer factual contexts through in-context retrieval. While effective i…
Open-Domain Question AnsweringQuestion AnsweringRAGRetrieval+1