打破边界：研究模型编辑对跨语言性能的影响
Breaking Boundaries: Investigating the Effects of Model Editing on Cross-linguistic Performance
AI总结：
本文评估多种知识编辑技术对Mistral等LLM跨语言一致性的影响，通过ELFI与ELFO压力测试揭示普通模型和合并模型的显著差异。
摘要：
BERT和GPT等预训练语言模型（PLM）的集成彻底改变了NLP，尤其是英语，但也造成了语言不平衡。本文通过考察多语言语境中的若干知识编辑技术，从战略上指出了语言公平的必要性。我们评估Mistral、TowerInstruct、OpenHathi、Tamil-Llama和Kan-Llama等模型在英语、德语、法语、意大利语、西班牙语、印地语、泰米尔语和卡纳达语等语言上的表现。研究发现普通模型与合并模型在跨语言一致性方面存在显著差异。我们采用“每种语言为自身”（ELFI）和“每种语言为其他语言”（ELFO）等策略对这些模型进行压力测试。结果证明LLM有潜力克服语言障碍，为未来在AI技术中实现语言包容性的研究奠定基础。
英文摘要：
The integration of pretrained language models (PLMs) like BERT and GPT has revolutionized NLP, particularly for English, but it has also created linguistic imbalances. This paper strategically identifies the need for linguistic equity by examining several knowledge editing techniques in multilingual contexts. We evaluate the performance of models such as Mistral, TowerInstruct, OpenHathi, Tamil-Llama, and Kan-Llama across languages including English, German, French, Italian, Spanish, Hindi, Tamil, and Kannada. Our research identifies significant discrepancies in normal and merged models concerning cross-lingual consistency. We employ strategies like 'each language for itself' (ELFI) and 'each language for others' (ELFO) to stress-test these models. Our findings demonstrate the potential for LLMs to overcome linguistic barriers, laying the groundwork for future research in achieving linguistic inclusivity in AI technologies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
