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

arXiv: https://arxiv.org/abs/2211.06993 · HTML: https://mlsanity.com/paper/2211.06993/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 68 references that are in the map.
- First posted on arXiv 13 Nov 2022.

## Builds on (most cited first, 30 of 36)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Scaling Language Models: Methods, Analysis & Insights from Training Gopher](https://mlsanity.com/paper/2112.11446/) (2021)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [How multilingual is Multilingual BERT?](https://mlsanity.com/paper/1906.01502/) (2019)
- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [On the Cross-lingual Transferability of Monolingual Representations](https://mlsanity.com/paper/1910.11856/) (2019)
- [Word Translation Without Parallel Data](https://mlsanity.com/paper/1710.04087/) (2017)
- [Are All Languages Created Equal in Multilingual BERT?](https://mlsanity.com/paper/2005.09093/) (2020)
- [Emerging Cross-lingual Structure in Pretrained Language Models](https://mlsanity.com/paper/1911.01464/) (2019)
- [MasakhaNER: Named Entity Recognition for African Languages](https://mlsanity.com/paper/2103.11811/) (2021)
- [Choosing Transfer Languages for Cross-Lingual Learning](https://mlsanity.com/paper/1905.12688/) (2019)
- [When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models](https://mlsanity.com/paper/2010.12858/) (2020)
- [ParsBERT: Transformer-based Model for Persian Language Understanding](https://mlsanity.com/paper/2005.12515/) (2020)
- [Language Contamination Helps Explain the Cross-lingual Capabilities of English Pretrained Models](https://mlsanity.com/paper/2204.08110/) (2022)
- [WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models](https://mlsanity.com/paper/2112.06598/) (2021)
- [bert2BERT: Towards Reusable Pretrained Language Models](https://mlsanity.com/paper/2110.07143/) (2021)
- [IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP](https://mlsanity.com/paper/2011.00677/) (2020)
- [As Good as New. How to Successfully Recycle English GPT-2 to Make Models for Other Languages](https://mlsanity.com/paper/2012.05628/) (2020)
- [How to Adapt Your Pretrained Multilingual Model to 1600 Languages](https://mlsanity.com/paper/2106.02124/) (2021)
- [The birth of Romanian BERT](https://mlsanity.com/paper/2009.08712/) (2020)

## Built on by (most cited first, 24 of 24)

- [Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning across Languages](https://mlsanity.com/paper/2310.14799/) (2023)
- [Benchmarking Large Language Models on CMExam -- A Comprehensive Chinese Medical Exam Dataset](https://mlsanity.com/paper/2306.03030/) (2023)
- [Qilin-Med-VL: Towards Chinese Large Vision-Language Model for General Healthcare](https://mlsanity.com/paper/2310.17956/) (2023)
- [Large Language Models in Mental Health Care: a Scoping Review](https://mlsanity.com/paper/2401.02984/) (2024)
- [STUN: Structured-Then-Unstructured Pruning for Scalable MoE Pruning](https://mlsanity.com/paper/2409.06211/) (2024)
- [Qilin-Med: Multi-stage Knowledge Injection Advanced Medical Large Language Model](https://mlsanity.com/paper/2310.09089/) (2023)
- [Exploring Design Choices for Building Language-Specific LLMs](https://mlsanity.com/paper/2406.14670/) (2024)
- [Efficiently Democratizing Medical LLMs for 50 Languages via a Mixture of Language Family Experts](https://mlsanity.com/paper/2410.10626/) (2024)
- [Embedding structure matters: Comparing methods to adapt multilingual vocabularies to new languages](https://mlsanity.com/paper/2309.04679/) (2023)
- [BRIDGE: Benchmarking Large Language Models for Understanding Real-world Clinical Practice Text](https://mlsanity.com/paper/2504.19467/) (2025)
- [CLINIC: Evaluating Multilingual Trustworthiness in Language Models for Healthcare](https://mlsanity.com/paper/2512.11437/) (2025)
- [BayLing 2: A Multilingual Large Language Model with Efficient Language Alignment](https://mlsanity.com/paper/2411.16300/) (2024)
- [Token Distillation: Attention-aware Input Embeddings For New Tokens](https://mlsanity.com/paper/2505.20133/) (2025)
- [CARE-MI: Chinese Benchmark for Misinformation Evaluation in Maternity and Infant Care](https://mlsanity.com/paper/2307.01458/) (2023)
- [Impact of Tokenization on LLaMa Russian Adaptation](https://mlsanity.com/paper/2312.02598/) (2023)
- [Continuous Training and Fine-tuning for Domain-Specific Language Models in Medical Question Answering](https://mlsanity.com/paper/2311.00204/) (2023)
- [MlingConf: A Comprehensive Study of Multilingual Confidence Estimation on Large Language Models](https://mlsanity.com/paper/2402.13606/) (2024)
- [A Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit](https://mlsanity.com/paper/2502.09097/) (2025)
- [Optimized Text Embedding Models and Benchmarks for Amharic Passage Retrieval](https://mlsanity.com/paper/2505.19356/) (2025)
- [Semantic Aware Linear Transfer by Recycling Pre-trained Language Models for Cross-lingual Transfer](https://mlsanity.com/paper/2505.10945/) (2025)
- [BrepLLM: Enabling Large Language Models to Understand Boundary Representations](https://mlsanity.com/paper/2512.16413/) (2025)
- [TokAlign++: Advancing Vocabulary Adaptation via Better Token Alignment](https://mlsanity.com/paper/2605.13429/) (2026)
- [FOCUS: Effective Embedding Initialization for Monolingual Specialization of Multilingual Models](https://mlsanity.com/paper/2305.14481/) (2023)
- [MlingConf: A Comprehensive Study of Multilingual Confidence Estimation on Large Language Models](https://mlsanity.com/paper/2410.12478/) (2024)

## Abstract (arXiv)

Large pre-trained models have revolutionized natural language processing (NLP) research and applications, but high training costs and limited data resources have prevented their benefits from being shared equally amongst speakers of all the world's languages. To address issues of cross-linguistic access to such models and reduce energy consumption for sustainability during large-scale model training, this study proposes an effective and energy-efficient framework called GreenPLM that uses bilingual lexicons to directly "translate" pre-trained language models of one language into another at almost no additional cost. We validate this approach in 18 languages' BERT models and show that this framework is comparable to, if not better than, other heuristics with high training costs. In addition, given lightweight continued pre-training on limited data where available, this framework outperforms the original monolingual language models in six out of seven tested languages with up to 200x less pre-training efforts. Aiming at the Leave No One Behind Principle (LNOB), our approach manages to reduce inequalities between languages and energy consumption greatly. We make our codes and models publicly available here: urlhttps://github.com/qcznlp/GreenPLMs