This is the repository for the NoDaLiDa 2023 paper on developing a Norwegian retrieval-augmented langauge model. The paper can be found at this url.
Retrieval-based language models are increasingly employed in question-answering tasks. These models search in a corpus of documents for relevant information instead of having all factual knowledge stored in its parameters, thereby enhancing efficiency, transparency, and adaptability. We develop the first Norwegian retrieval-based model by adapting the REALM framework and evaluate it on various tasks. After training, we also separate the language model, which we call the reader, from the retriever components, and show that this can be fine-tuned on a range of downstream tasks. Results show that retrieval augmented language modeling improves the reader's performance on extractive question-answering, suggesting that this type of training improves language models' general ability to use context and that this does not happen at the expense of other abilities such as part-of-speech tagging, dependency parsing, named entity recognition, and lemmatization.
BRENT was a joint work between Lucas Georges Gabriel Charpentier, Sondre Wold, David Samuel and Egil Rønningstad.
If you publish work that uses or references the data, please cite our NODALIDA article. BibEntry:
@InProceedings{charpentier-etal-2023brent,
{Charpentier, Lucas Georges Gabriel and Wold, Sondre and Samuel, David and R{\o}nningstad, Egil},
title = {BRENT: Bidirectional Retrieval Enhanced Norwegian Transformer},
booktitle = {Proceedings of the 24th Nordic Conference on Computational Linguistics},
year = {2023},
address = {Torshavn, Faroe Islands},
}