MLaKE: Multilingual Knowledge Editing Benchmark for Large Language Models

• URL: http://arxiv.org/abs/2404.04990v2
• Date: Tue, 08 Oct 2024 01:58:48 GMT
• Title: MLaKE: Multilingual Knowledge Editing Benchmark for Large Language Models
• Authors: Zihao Wei, Jingcheng Deng, Liang Pang, Hanxing Ding, Huawei Shen, Xueqi Cheng,
• Abstract summary: MLaKE is a benchmark for the adaptability of knowledge editing methods across five languages. MLaKE aggregates fact chains from Wikipedia across languages and generates questions in both free-form and multiple-choice. We evaluate the multilingual knowledge editing generalization capabilities of existing methods on MLaKE.
• Score: 65.10456412127405
• License:
• 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 languages: English, Chinese, Japanese, French, and German. MLaKE aggregates fact chains from Wikipedia across languages and utilizes LLMs to generate questions in both free-form and multiple-choice. We evaluate the multilingual knowledge editing generalization capabilities of existing methods on MLaKE. Existing knowledge editing methods demonstrate higher success rates in English samples compared to other languages. However, their generalization capabilities are limited in multi-language experiments. Notably, existing knowledge editing methods often show relatively high generalization for languages within the same language family compared to languages from different language families. These results underscore the imperative need for advancements in multilingual knowledge editing and we hope MLaKE can serve as a valuable resource for benchmarking and solution development.

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