MLaKE: Multilingual Knowledge Editing Benchmark for Large Language Models

arXiv:2404.04990v1 Announce Type: new
Abstract: The extensive utilization of large language models (LLMs) underscores the crucial necessity for precise and contemporary knowledge embedded within their intrinsic parameters. Existing research on knowledge editing primarily concentrates on monolingual scenarios, neglecting the complexities presented by multilingual contexts and multi-hop reasoning. To address these challenges, our study introduces MLaKE (Multilingual Language Knowledge Editing), a novel benchmark comprising 4072 multi-hop and 5360 single-hop questions designed to evaluate the adaptability of knowledge editing methods across five …

abstract arxiv benchmark challenges complexities cs.cl editing embedded intrinsic knowledge language language models large language large language models llms multilingual parameters reasoning research type