Year: 2025
Type: conference-paper
Abstract: The curse of multilinguality phenomenon is a fundamental problem of multilingual Large Language Models (LLMs), where the competition between massive languages results in inferior performance.It mainly comes from limited capacity and negative transfer between dissimilar languages.To address this issue, we propose a method to dynamically group and scale up the parameters of multilingual LLM while boosting positive transfer among similar languages.Specifically, the model is first tuned on monolingual corpus to determine the parameter deviation in each layer and quantify the similarity between languages.Layers with more deviations are extended to mixture-of-experts layers to reduce competition between languages, where one expert module serves one group of similar languages.Experimental results on 18 to 128 languages show that our method reduces the negative transfer between languages and significantly boosts multilingual performance with fewer parameters.Such language group specialization on experts benefits the new language adaptation and reduces the inference on the previous multilingual knowledge learned. 1 * Corresponding author. 1 Our code and model weights are available at https://git hub.com/ZNLP/DMoE ta de ||∆𝜃 𝑙 𝑥 || = ||𝜃 𝑙 0 -𝜃 𝑙 𝑥 || 𝑓𝑜𝑟 𝑙𝑎𝑦𝑒𝑟 𝑙 ∈ [1, 𝑁] 𝒍 5 4 3 2 1 … … id (a) Calculate the parameter deviation for each language.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Award: G1883487206
OpenAlex ID: W4412888790