1 citations · 2 across the 5 of their papers we have counts for
6 papers
Your Prompt Should Do More: Effects of Retrieval Instructions in Embedding Models
Amanda Myntti, Jenna Kanerva, Veronika Laippala +1
Prompted embedding models have recently received increasing attention, particularly for retrieval, where detailed retrieval instructions are provided as part of the retrieval promp…
Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance
Amanda Myntti, Jenna Kanerva, Veronika Laippala +1
In this paper, we show that high-performing embedding models organize their embedding spaces in a consistent way. We evaluate 25 contemporary embedding models on five MTEB tasks sp…
HPLT 3.0: Very Large-Scale Multilingual Resources for LLMs and MT. Mono- and Bi-lingual Data, Multilingual Evaluation, and Pre-Trained Models
Stephan Oepen, Nikolay Arefev, Mikko Aulamo +29
We present an ongoing initiative to provide open, very large, high-quality, and richly annotated textual datasets for almost 200 languages. At 30 trillion tokens, this is likely th…
Register Always Matters: Analysis of LLM Pretraining Data Through the Lens of Language Variation
Amanda Myntti, Erik Henriksson, Veronika Laippala +1
Pretraining data curation is a cornerstone in Large Language Model (LLM) development, leading to growing research on quality filtering of large web corpora. From statistical qualit…
Automatic register identification for the open web using multilingual deep learning
Erik Henriksson, Amanda Myntti, Saara Hellström +3
This article presents multilingual deep learning models for identifying web registers -- text varieties such as news reports and discussion forums -- across 16 languages. We introd…
Explaining Classes through Word Attribution
Samuel Rönnqvist, Amanda Myntti, Aki-Juhani Kyröläinen +3
In recent years, several methods have been proposed for explaining individual predictions of deep learning models, yet there has been little study of how to aggregate these predict…