Summary of Cross-lingual Editing in Multilingual Language Models, by Himanshu Beniwal et al.
Cross-lingual Editing in Multilingual Language Models
by Himanshu Beniwal, Kowsik Nandagopan D, Mayank Singh
First submitted to arxiv on: 19 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a new paradigm for updating large language models (LLMs) called cross-lingual model editing (XME). XME allows for the efficient update of model outputs without retraining, which is particularly important when dealing with multilingual LLMs that store knowledge in diverse languages. The authors conducted experiments using BLOOM, mBERT, and XLM-RoBERTa models on two writing scripts: Latin (English, French, and Spanish) and Indic (Hindi, Gujarati, and Bengali). Their results show notable performance limitations of state-of-the-art model editing techniques under the XME setting, particularly when languages belong to different script families. These findings highlight the need for further research and development of XME techniques to address these challenges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about updating big language models without needing to retrain them from scratch. The researchers want to find a way to do this efficiently, especially when dealing with models that know many languages. They tested different methods on two types of writing scripts: those used in European languages like English and Spanish, and those used in Indian languages like Hindi and Bengali. Their results show that current methods don’t work very well for updating language models across different languages and writing systems. |
