title: Breaking Boundaries: Investigating the Effects of Model Editing on
    Cross-linguistic Performance
summary: 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.
paper_authors._id: 667323bc722e4b59501c1870
paper_authors.name: Somnath Banerjee
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paper_authors.fullname: Somnath Banerjee
paper_authors.type: user
paper_authors.user: leowin
paper_authors._id: 667323bc722e4b59501c1871
paper_authors.name: Avik Halder
paper_authors._id: 667323bc722e4b59501c1872
paper_authors.name: Rajarshi Mandal
paper_authors.status: admin_assigned
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paper_authors.fullname: Rajarshi Mandal
paper_authors.type: user
paper_authors.user: Rajarshi02
paper_authors._id: 667323bc722e4b59501c1873
paper_authors.name: Sayan Layek
paper_authors.status: admin_assigned
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paper_authors.fullname: Sayan Layek
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paper_authors.user: caprion
paper_authors._id: 667323bc722e4b59501c1874
paper_authors.name: Ian Soboroff
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paper_authors.fullname: Ian Soboroff
paper_authors.type: user
paper_authors.user: isoboroff
paper_authors._id: 667323bc722e4b59501c1875
paper_authors.name: Rima Hazra
paper_authors.status: admin_assigned
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paper_authors.fullname: RIMA HAZRA
paper_authors.type: user
paper_authors.user: rimahazra
paper_authors._id: 667323bc722e4b59501c1876
paper_authors.name: Animesh Mukherjee
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-20T12:44:02.497Z
paper_authors._id: 5ebd450338bcf80db421091e
paper_authors.avatarUrl: /avatars/13a1945aa90a7521c413ae0eb7b3228a.svg
paper_authors.fullname: Animesh Mukherjee
paper_authors.type: user
paper_authors.user: animeshm
paper_title: Breaking Boundaries: Investigating the Effects of Model Editing on
    Cross-linguistic Performance
paper_summary: 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.
paper_ai_summary: Evaluation of knowledge editing techniques in multilingual pretrained language models reveals discrepancies in cross-lingual consistency, using strategies to stress-test and improve linguistic inclusivity.
paper_ai_keywords: pretrained language models BERT GPT Mistral TowerInstruct OpenHathi Tamil-Llama Kan-Llama cross-lingual consistency each language for itself each language for others