Zhenpeng Su, Leiyu Pan, Minxuan Lv, Yuntao Li, Wenping Hu, Fuzheng Zhang, Kun Gai, Guorui Zhou. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Lei Yang, Leiyu Pan, Bojian Xiong, Renren Jin, Shaowei Zhang, Yue Chen, Ling Shi, Jiang Zhou, Junru Wu, Zhen Wang, Jianxiang Peng, Juesi Xiao, Tianyu Dong, Zhuowen Han, Zhuo Chen, Yuqi Ren, Deyi Xiong. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Minxuan Lv, Zhenpeng Su, Leiyu Pan, Yizhe Xiong, Zijia Lin, Hui Chen, Wei Zhou, Jungong Han, Guiguang Ding, Wenwu Ou, Di Zhang, Kun Gai, Songlin Hu. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Recent advancements in large language models (LLMs) have shown promising results in multilingual translation even with limited bilingual supervision.The major challenges are catastrophic forgetting and parameter interference 1 for finetuning LLMs when provided parallel training data.To address these challenges, we propose LANDeRMT, a Language-Aware Neuron Detecting and Routing framework that selectively finetunes LLMs to Machine Translation with diverse translation training data.In LANDeRMT, we evaluate the awareness of neurons to MT tasks and categorize them into language-general and languagespecific neurons.This categorization enables selective parameter updates during finetuning, mitigating parameter interference and catastrophic forgetting issues.For the detected neurons, we further propose a conditional awareness-based routing mechanism to dynamically adjust language-general and languagespecific capacity within LLMs, guided by translation signals.Experimental results demonstrate that the proposed LANDeRMT is very effective in learning translation knowledge, significantly improving translation quality over various strong baselines for multiple language pairs.

Large language models (LLMs) have been used for machine translation. When provided with prompts and source sentences, LLMs can achieve impressive translation results. However, the robustness of these LLMs remains a significant challenge, as they often struggle to accurately translate sentences in the presence of noise, even when using similarity-based in-context learning methods. This work proposes a research scheme for studying machine translation robustness on LLMs, investigating whether LLMs can learn translation robustness from noisy-source demonstration examples. Through experiments on different models, languages, and noise types, we empirically demonstrate that LLMs can learn how to handle noise and translation methods from noisy-source demonstration examples, thereby improving their translation performance on noisy sentences. Furthermore, we find that increasing the noise ratio appropriately for the noisy-source demonstration examples can enhance the translation robustness of LLMs. Additionally, we also attempt to investigate scenarios where LLMs are more likely to learn translation robustness for mixed and specific types of noise. We find that the model’s performance varies across different noise settings.

Massively multilingual neural machine translation (MMNMT) has been proven to enhance the translation quality of low-resource languages. In this paper, we empirically investigate the translation robustness of Indonesian-Chinese translation in the face of various naturally occurring noise. To assess this, we create a robustness evaluation benchmark dataset for Indonesian-Chinese translation. This dataset is automatically translated into Chinese using four NLLB-200 models of different sizes. We conduct both automatic and human evaluations. Our in-depth analysis reveal the correlations between translation error types and the types of noise present, how these correlations change across different model sizes, and the relationships between automatic evaluation indicators and human evaluation indicators. The dataset is publicly available at https://github.com/tjunlp-lab/ID-ZH-MTRobustEval.