331 citations · 600 across the 21 of their papers we have counts for
24 papers
Deriving Scaling Laws for OpenEuroLLM Models: Learning Rate, Batch Size and Loss
Niccolò Ajroldi, Diana Alexandra Onutu, Haider Al-Tahan +4
We study the scaling behavior of learning rate and batch size in pretraining dense large language models on English-prevalent corpora. Beyond scaling jointly optimal learning rates…
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…
Question Answering models for information extraction from perovskite materials science literature
M. Sipilä, F. Mehryary, S. Pyysalo +2
Scientific text is a promising source of data in materials science, with ongoing research into utilising textual data for materials discovery. In this study, we developed and teste…
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…