突破界限：探討模型編輯對跨語言表現的影響

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

June 17, 2024

作者: Somnath Banerjee, Avik Halder, Rajarshi Mandal, Sayan Layek, Ian Soboroff, Rima Hazra, Animesh Mukherjee

cs.AI

摘要

預訓練語言模型（PLMs）如BERT和GPT的整合已經在自然語言處理（NLP）領域引起革命，尤其是對於英語，但也造成了語言上的不平衡。本文從策略角度指出了在多語境下檢視多種知識編輯技術以實現語言平等的需求。我們評估了Mistral、TowerInstruct、OpenHathi、Tamil-Llama和Kan-Llama等模型在包括英語、德語、法語、意大利語、西班牙語、印地語、泰米爾語和坎納達語在內的多種語言上的表現。我們的研究發現了關於跨語言一致性的正常模型和合併模型之間存在顯著差異。我們採用“每種語言為自己”（ELFI）和“每種語言為他人”（ELFO）等策略來對這些模型進行壓力測試。我們的研究結果顯示了大型語言模型（LLMs）克服語言障礙的潛力，為未來在實現AI技術中的語言包容性方面奠定了基礎。

English

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.