331 citations · 600 across the 21 of their papers we have counts for
22 papers · 1 filter
Got Compute, but No Data: Lessons From Post-training a Finnish LLM
Elaine Zosa, Ville Komulainen, Sampo Pyysalo
As LLMs gain more popularity as chatbots and general assistants, methods have been developed to enable LLMs to follow instructions and align with human preferences. These methods h…
A Survey of Large Language Models for European Languages
Wazir Ali, Sampo Pyysalo
Large Language Models (LLMs) have gained significant attention due to their high performance on a wide range of natural language tasks since the release of ChatGPT. The LLMs learn…
Poro 34B and the Blessing of Multilinguality
Risto Luukkonen, Jonathan Burdge, Elaine Zosa +5
The pretraining of state-of-the-art large language models now requires trillions of words of text, which is orders of magnitude more than available for the vast majority of languag…
Aurora-M: Open Source Continual Pre-training for Multilingual Language and Code
Taishi Nakamura, Mayank Mishra, Simone Tedeschi +42
Pretrained language models are an integral part of AI applications, but their high computational cost for training limits accessibility. Initiatives such as Bloom and StarCoder aim…
A New Massive Multilingual Dataset for High-Performance Language Technologies
Ona de Gibert, Graeme Nail, Nikolay Arefyev +10
We present the HPLT (High Performance Language Technologies) language resources, a new massive multilingual dataset including both monolingual and bilingual corpora extracted from…
FinGPT: Large Generative Models for a Small Language
Risto Luukkonen, Ville Komulainen, Jouni Luoma +18
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 wh…