The Problem with Multilingual Language Models: What are Cross-Lingual Expert Language Models (X-ELM) and Why Do They Matter?
Large-scale multilingual language models have become the backbone of many cross-lingual and non-English Natural Language Processing (NLP) applications. However, these models, which are trained in multiple languages, are not without problems. The “curse of multilingualism” happens when these models compete for limited capacity, resulting in weaker performance in individual languages.
To address this issue, a team of researchers introduced Cross-lingual Expert Language Models (X-ELM). Unlike multilingual models, X-ELM allows each language model to specialize in particular subsets of multilingual data, reducing the competition for model parameters and performance conflicts.
This approach significantly improves the model’s capacity to reflect the nuances of different languages more accurately. It also introduces a new method called x-BTM, which enhances the training paradigm to more effectively educate new experts with specialized knowledge and adapt to new languages without sacrificing performance.
The team’s research papers present experiments involving twenty languages and demonstrate that X-ELM consistently outperforms conventional multilingual models. The model’s application extends to real-world scenarios and allows additional languages to be incorporated without forgetting the previously learned languages.
X-ELM offers a promising solution to the challenges of using multilingual language models. The research paper by the team can be found here. For more insights, make sure to check out the researcher’s work on their respective social media platforms.