Breaking Boundaries: Investigating the Effects of Model Editing on
  Cross-linguistic Performance
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.
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Somnath Banerjee
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2024-06-20T12:42:55.829Z
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Somnath Banerjee
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leowin
667323bc722e4b59501c1871
Avik Halder
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Rajarshi Mandal
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2024-06-20T12:43:24.478Z
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Rajarshi Mandal
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Rajarshi02
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Sayan Layek
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2024-06-20T12:43:44.332Z
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Sayan Layek
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caprion
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Ian Soboroff
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2024-06-20T12:43:51.270Z
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Ian Soboroff
user
isoboroff
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Rima Hazra
admin_assigned
2024-06-20T12:43:56.772Z
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RIMA HAZRA
user
rimahazra
667323bc722e4b59501c1876
Animesh Mukherjee
admin_assigned
2024-06-20T12:44:02.497Z
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Animesh Mukherjee
user
animeshm
Breaking Boundaries: Investigating the Effects of Model Editing on
  Cross-linguistic Performance
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.
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.
pretrained language models BERT GPT Mistral TowerInstruct OpenHathi Tamil-Llama Kan-Llama cross-lingual consistency each language for itself each language for others