Cross-lingual Editing in Multilingual Language Models (2401.10521v2)

Abstract: The training of LLMs necessitates substantial data and computational resources, and updating outdated LLMs entails significant efforts and resources. While numerous model editing techniques (METs) have emerged to efficiently update model outputs without retraining, their effectiveness in multilingual LLMs, where knowledge is stored in diverse languages, remains an underexplored research area. This research paper introduces the cross-lingual model editing (\textbf{XME}) paradigm, wherein a fact is edited in one language, and the subsequent update propagation is observed across other languages. To investigate the XME paradigm, we conducted experiments using BLOOM, mBERT, and XLM-RoBERTa using the two writing scripts: \textit{Latin} (English, French, and Spanish) and \textit{Indic} (Hindi, Gujarati, and Bengali). The results reveal notable performance limitations of state-of-the-art METs under the XME setting, mainly when the languages involved belong to two distinct script families. These findings highlight the need for further research and development of XME techniques to address these challenges. For more comprehensive information, the dataset used in this research and the associated code are publicly available at the following URL\url{https://github.com/lingo-iitgn/XME}.

• The paper demonstrates that cross-lingual model editing exposes limitations in METs when updating facts across distinct script families.
• It reveals that encoder-only architectures localize updated information in later layers, whereas decoder-only models distribute knowledge across middle layers.
• The study finds that choosing a fine-tuning language from the same script family significantly enhances cross-lingual editing performance.

Cross-lingual Editing in Multilingual LLMs: An Analysis

The paper, "Cross-lingual Editing in Multilingual LLMs," investigates the challenges and methodologies for efficiently updating multilingual LLMs without extensive retraining. Such retraining efforts pose substantial demands in terms of computational resources and data. Specifically, the research introduces the concept of Cross-lingual Model Editing (XME) where a fact updated in one language propagates its effects across other languages. This paper is vital in understanding the capabilities and limitations of current model editing techniques (METs) in a multilingual setting.

Overview of Methodology

The researchers methodically evaluate the XME paradigm using LLMs like BLOOM, mBERT, and XLM-RoBERTa, which encode multilingual capabilities. Languages examined include English, French, Spanish, Hindi, Gujarati, and Bengali, representing both Latin and Indic script families. The paper meticulously assesses how state-of-the-art METs, designed initially for monolingual scenarios, perform under cross-lingual constraints.

Key Findings

1. Effectiveness of METs in Cross-lingual Settings: The empirical results underscore a discernible decline in the generability score ($G_S$) for cross-lingual contexts versus monolingual settings. METs exhibit limited efficacy when languages span different script families. However, encoder-only models like mBERT and XLM-RoBERTa demonstrate reasonably consistent $G_S$ scores within the same script family.
2. Knowledge Localization in Different Architectures: The paper reveals that factual information tends to localize differently in encoder-only and decoder-only architectures. For instance, encoder-only models predominantly store information in their last layers, whereas decoder-only models like BLOOM localize information across middle layers.
3. Impact of Initial Fine-tuning Language Choice: The paper articulates the profound impact of fine-tuning language selection on editing performance across languages. It suggests that editing performance is superior when initial fine-tuning favors languages from the same script family as the target.
4. Comparison with Traditional Fine-tuning: The performance of traditional fine-tuning approximates that of METs in cross-lingual settings, challenging prior findings of MET superiority in monolingual tasks.

Implications and Future Directions

The findings of this paper carry significant implications for the development and optimization of XME techniques. The observed challenges highlight the necessity for METs tailored to cross-lingual scenarios, wherein updates in one language seamlessly adapt across other languages in multilingual models. Additionally, the research identifies the potential for parameter-preserving and localized techniques to enhance cross-lingual model editing capabilities. Extending tools and datasets to encompass broader NLP tasks, like machine translation and question answering, represents an exciting direction for future research. Enhanced cross-lingual editing systems may foster advances in multilingual understanding, enabling LLMs to adapt more flexibly to rapidly evolving knowledge across global languages.

In conclusion, the research critically evaluates the current landscape of cross-lingual model editing, providing valuable insights into the emergent challenges and limitations within multilingual LLMs. These insights serve as a crucial stepping stone toward more effective multilingual NLP models that can be updated with minimal resource expenditure and maximum performance retention.