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.

该研究通过考察多语境下的几种知识编辑技术，战略性地确定了语言平等的需求。我们评估了 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性能。研究发现了跨语言一致性方面正常模型和合并模型之间的显著差异。我们采用 “每种语言为自己”（ELFI）和 “每种语言为他人”（ELFO）等策略对这些模型进行了强化测试。研究结果表明，LMM 具有克服语言障碍的潜力，为实现人工智能技术中的语言包容性奠定了基础。