Hanna-Mari Kupari


2023

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FinGPT: Large Generative Models for a Small Language
Risto Luukkonen | Ville Komulainen | Jouni Luoma | Anni Eskelinen | Jenna Kanerva | Hanna-Mari Kupari | Filip Ginter | Veronika Laippala | Niklas Muennighoff | Aleksandra Piktus | Thomas Wang | Nouamane Tazi | Teven Scao | Thomas Wolf | Osma Suominen | Samuli Sairanen | Mikko Merioksa | Jyrki Heinonen | Aija Vahtola | Samuel Antao | Sampo Pyysalo
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Large language models (LLMs) excel in many tasks in NLP and beyond, but most open models have very limited coverage of smaller languages and LLM work tends to focus on languages where nearly unlimited data is available for pretraining. In this work, we study the challenges of creating LLMs for Finnish, a language spoken by less than 0.1% of the world population. We compile an extensive dataset of Finnish combining web crawls, news, social media and eBooks. We pursue two approaches to pretrain models: 1) we train seven monolingual models from scratch (186M to 13B parameters) dubbed FinGPT, 2) we continue the pretraining of the multilingual BLOOM model on a mix of its original training data and Finnish, resulting in a 176 billion parameter model we call BLUUMI. For model evaluation, we introduce FIN-bench, a version of BIG-bench with Finnish tasks. We also assess other model qualities such as toxicity and bias. Our models and tools are openly available at https://turkunlp.org/gpt3-finnish.

2021

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Finnish Paraphrase Corpus
Jenna Kanerva | Filip Ginter | Li-Hsin Chang | Iiro Rastas | Valtteri Skantsi | Jemina Kilpeläinen | Hanna-Mari Kupari | Jenna Saarni | Maija Sevón | Otto Tarkka
Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)

In this paper, we introduce the first fully manually annotated paraphrase corpus for Finnish containing 53,572 paraphrase pairs harvested from alternative subtitles and news headings. Out of all paraphrase pairs in our corpus 98% are manually classified to be paraphrases at least in their given context, if not in all contexts. Additionally, we establish a manual candidate selection method and demonstrate its feasibility in high quality paraphrase selection in terms of both cost and quality.