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.

翻译：以BERT和GPT为代表的预训练语言模型（PLM）的集成已经彻底改变了自然语言处理领域，尤其对于英语而言，但同时也造成了语言上的不平衡。本文通过在多语言语境下考察多种知识编辑技术，战略性地指出了实现语言公平的必要性。我们评估了包括Mistral、TowerInstruct、OpenHathi、Tamil-Llama和Kan-Llama在内的模型在英语、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多种语言上的性能。我们的研究发现，常规模型与融合模型在跨语言一致性方面存在显著差异。我们采用了诸如“各语言独立优化”（ELFI）和“各语言协同优化”（ELFO）等策略对这些模型进行压力测试。我们的研究结果表明，大语言模型具备克服语言障碍的潜力，这为未来在人工智能技术中实现语言包容性的研究奠定了基础。