Reducing the carbon footprint of natural language processing

The GreenNLP project is building resources for sustainable NLP

The recent dramatic advances in natural language processing (NLP) technology, such as neural machine translation (NMT) and large language models (LLM), are changing the way people work and interact with technology. These new NLP technologies have the potential to increase productivity and levels of automation in a wide variety of fields.

The downside of the new NLP technology is its enormous energy consumption. At a time when energy efficiency has become essential due to the climate crisis, the advances in NLP are vastly increasing the energy usage of the IT sector. The GreenNLP project addresses this issue by developing more environmentally sustainable ways of building and using NLP applications.

News from GreenNLP

NVIDIA AI Technology Center NLP Seminar + Demo on May 16

Join us on May 16 at 10:00-13:00 in Helsinki (or online) for a session hosted by GreenNLP consortium (University of Helsinki, University of Turku, CSC) and the NVIDIA AI Technology Center (NVAITC) Finland. The event highlights developments in NLP technologies, including an overview and demo of NVIDIA’s NeMo framework.

                  

17 April 2025

GreenNLP papers at NoDaLiDa/Baltic-HLT 2025

GreenNLP has a strong representation at the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025). GreenNLP researchers will present four papers showcasing the multifaceted research done within the project.

                  

24 January 2025

Moving your AI training jobs to LUMI: A Hands-On Workshop

The GreenNLP project is organizing a workshop on AI training on the LUMI supercomputer together with the LUMI User Support Team (LUST) and EuroCC National Competence Centers (NCCs) in Finland.

                  

18 December 2024

More news from GreenNLP...

Areas of research

Data curation

Reducing training costs through data curation and selection

Efficient use of LLMs

Decreasing runtime costs by using LLMs efficiently

Compact translation models

Decreasing runtime costs with compact translation models

Efficient computation

Reducing computation with efficient training and inference procedures

Modular NLP

Cost-efficient components with modular multilingual NLP

Reuse and sustainability

Documentation, packaging and distribution

Consortium partners