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

arXiv: https://arxiv.org/abs/2406.11139 · HTML: https://mlsanity.com/paper/2406.11139/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 39 references that are in the map.
- First posted on arXiv 17 Jun 2024.

## Builds on (most cited first, 20 of 20)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Locating and Editing Factual Associations in GPT](https://mlsanity.com/paper/2202.05262/) (2022)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [Mass-Editing Memory in a Transformer](https://mlsanity.com/paper/2210.07229/) (2022)
- [Editing Factual Knowledge in Language Models](https://mlsanity.com/paper/2104.08164/) (2021)
- [Zero-Shot Relation Extraction via Reading Comprehension](https://mlsanity.com/paper/1706.04115/) (2017)
- [Editing Large Language Models: Problems, Methods, and Opportunities](https://mlsanity.com/paper/2305.13172/) (2023)
- [Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models](https://mlsanity.com/paper/2301.04213/) (2023)
- [Tower: An Open Multilingual Large Language Model for Translation-Related Tasks](https://mlsanity.com/paper/2402.17733/) (2024)
- [Editable Neural Networks](https://mlsanity.com/paper/2004.00345/) (2020)
- [Multilingual LAMA: Investigating Knowledge in Multilingual Pretrained Language Models](https://mlsanity.com/paper/2102.00894/) (2021)
- [Cross-Lingual Consistency of Factual Knowledge in Multilingual Language Models](https://mlsanity.com/paper/2310.10378/) (2023)
- [X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained Language Models](https://mlsanity.com/paper/2010.06189/) (2020)
- [A Survey on Multilingual Large Language Models: Corpora, Alignment, and Bias](https://mlsanity.com/paper/2404.00929/) (2024)
- [Tamil-Llama: A New Tamil Language Model Based on Llama 2](https://mlsanity.com/paper/2311.05845/) (2023)
- [How (un)ethical are instruction-centric responses of LLMs? Unveiling the vulnerabilities of safety guardrails to harmful queries](https://mlsanity.com/paper/2402.15302/) (2024)
- [Language Anisotropic Cross-Lingual Model Editing](https://mlsanity.com/paper/2205.12677/) (2022)
- [Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages](https://mlsanity.com/paper/2204.12543/) (2022)
- [Sowing the Wind, Reaping the Whirlwind: The Impact of Editing Language Models](https://mlsanity.com/paper/2401.10647/) (2024)
- [Cross-lingual Editing in Multilingual Language Models](https://mlsanity.com/paper/2401.10521/) (2024)

## Built on by (most cited first, 4 of 4)

- [Attributional Safety Failures in Large Language Models under Code-Mixed Perturbations](https://mlsanity.com/paper/2505.14469/) (2025)
- [Navigating the Cultural Kaleidoscope: A Hitchhiker's Guide to Sensitivity in Large Language Models](https://mlsanity.com/paper/2410.12880/) (2024)
- [Tutoring Large Language Models to be Domain-adaptive, Precise, and Safe](https://mlsanity.com/paper/2602.13860/) (2026)
- [Tutoring Large Language Models to be Domain-adaptive, Precise and Safe](https://mlsanity.com/paper/2609.23071/) (2026)

## Abstract (arXiv)

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.