New Research Explores How to Boost Large Language Models’ Multilingual Performance
In a February 20, 2025 paper , researchers Danni Liu and Jan Niehues from the Karlsruhe Institute of Technology proposed a way to improve how large language models (LLMs) perform across different languages. New Research Explores How to Boost Large Language Models’ Multilingual Performance They explained that LLMs like Llama 3 and Qwen 2.5, show strong performance in tasks like machine translation (MT) but often struggle with low-resource languages due to limited available data. Current fine-tuning processes do not effectively bridge the performance gaps across diverse languages, making it difficult for models to generalize effectively beyond high-resource settings. The researchers focus on leveraging the middle layers of LLMs to enable better cross-lingual transfer across multiple tasks, including MT. LLMs consist of multiple layers . The early (or bottom) layers handle basic patterns like individual words, while the final (or top) layers focus on producing a response. The midd...