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。

跨界拓展：模型编辑对跨语言性能的影响研究

Breaking Boundaries: Investigating the Effects of Model Editing on  Cross-linguistic Performance

Large language models (LLMs) finetuned to follow human instructions have
recently emerged as a breakthrough in AI. Models such as Google Bard and OpenAI
ChatGPT, for example, are surprisingly powerful tools for question answering,
code debugging, and dialogue generation. Despite the purported multilingual
proficiency of these models, their linguistic inclusivity remains
insufficiently explored. Considering this constraint, we present a thorough
assessment of Bard and ChatGPT (encompassing both GPT-3.5 and GPT-4) regarding
their machine translation proficiencies across ten varieties of Arabic. Our
evaluation covers diverse Arabic varieties such as Classical Arabic, Modern
Standard Arabic, and several nuanced dialectal variants. Furthermore, we
undertake a human-centric study to scrutinize the efficacy of the most recent
model, Bard, in following human instructions during translation tasks. Our
exhaustive analysis indicates that LLMs may encounter challenges with certain
Arabic dialects, particularly those for which minimal public data exists, such
as Algerian and Mauritanian dialects. However, they exhibit satisfactory
performance with more prevalent dialects, albeit occasionally trailing behind
established commercial systems like Google Translate. Additionally, our
analysis reveals a circumscribed capability of Bard in aligning with human
instructions in translation contexts. Collectively, our findings underscore
that prevailing LLMs remain far from inclusive, with only limited ability to
cater for the linguistic and cultural intricacies of diverse communities.

综述了大型语言模型对阿拉伯语方言的机器翻译效能以及在翻译任务中与人类指令的对齐能力，发现大型语言模型在处理某些阿拉伯语方言时可能面临挑战，尤其是针对缺乏公开数据的阿尔及利亚和毛里塔尼亚方言，但对更普遍的方言表现令人满意，虽然偶尔落后于像 Google 翻译这样的商业系统。总体而言，现有的大型语言模型在包容性方面仍有待改进，仅具有有限的能力来满足不同社区之间的语言和文化复杂性需求。