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Large pre-trained models have revolutionized natural language processing(NLP) research and applications, but high training costs and limited dataresources have prevented their benefits from being shared equally amongstspeakers of all the world's languages. To address issues of cross-linguisticaccess to such models and reduce energy consumption for sustainability duringlarge-scale model training, this study proposes an effective andenergy-efficient framework called GreenPLM that uses bilingual lexicons todirectly "translate" pre-trained language models of one language into anotherat almost no additional cost. We validate this approach in 18 languages' BERTmodels and show that this framework is comparable to, if not better than, otherheuristics with high training costs. In addition, given lightweight continuedpre-training on limited data where available, this framework outperforms theoriginal monolingual language models in six out of seven tested languages withup to 200x less pre-training efforts. Aiming at the Leave No One BehindPrinciple (LNOB), our approach manages to reduce inequalities between languagesand energy consumption greatly. We make our codes and models publicly availablehere: \url{https://github.com/qcznlp/GreenPLMs}

GreenPLM: Cross-Lingual Transfer of Monolingual Pre-Trained Language Models at Almost No Cost