Large-scale multilingual language models form the basis of many cross-lingual and non-English NLP applications. However, their use leads to a performance decline in individual languages due to inter-language competition for model capacity. To address this, researchers from the University of Washington, Charles University, and the Allen Institute propose Cross-lingual Expert Language Models (X-ELM), which aim to reduce this conflict by enabling autonomous specialization of each language model. The X-ELM outperforms jointly trained multilingual models in all languages considered, even with the same computational resources. The research paper presents X-ELM as a potential solution to challenges with massive multilingual language models.
Cross-lingual Expert Language Models (X-ELM): Overcoming Multilingual Model Limitations
Are you facing challenges with multilingual language models? Researchers from the University of Washington, Charles University in Prague, and the Allen Institute for Artificial Intelligence have proposed a solution in the form of Cross-lingual Expert Language Models (X-ELM).
Practical Solutions and Value:
X-ELM aims to address the limitations of large-scale multilingual language models by allowing autonomous specialization of each language model in the ensemble on a particular subset of the multilingual data. This approach reduces inter-language conflict for model parameters.
The team has shown that X-ELM outperforms jointly trained, multilingual models in all languages considered when given the same computational resources. The model’s performance improvements also apply to real-world scenarios.
X-ELM’s capability to dynamically select experts for inference and adjust to new languages without forgetting previously learned languages makes it a valuable solution for middle managers looking to leverage AI.
Key Takeaways:
- X-ELM addresses difficulties in using massive multilingual language models.
- It outperforms jointly trained, multilingual models in various languages.
- Offers practical solutions for middle managers interested in AI.
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