6 papers · 1 filter
Language Exposure Matters: Distribution and Order in Multilingual BabyLM Pre-training
JiWoo Hwang, Lukas Edman, Alexander Fraser
We investigate two aspects of multilingual pre-training with limited data, namely how language exposure is distributed across English, Dutch, and Chinese, and how that exposure is…
Qwenfant: Train Big Character-level BabyLMs on Concise Parallel Text
Lukas Edman, Alexander Fraser
This work concerns our submission for the Multilingual track of the 2026 BabyLM Challenge. We improve on our baseline using data that has been summarized and translated by an LLM,…
Toppling the Hierarchy in Byte-level Language Modeling
Lukas Edman, Alexander Fraser
This work examines recent byte-level models and their failure to perfectly manipulate characters. State-of-the-art byte-level models use a hierarchical structure, starting at the b…
Squeezing More from Limited Data with Recursive Transformers
Serdar Gülbahar, Lukas Edman, Alexander Fraser
Pre-training under limited data requires a different view of scaling than web-scale language modeling. With a fixed data budget but relatively abundant compute, increasing paramete…
From Specialization to Generalization: Instruction-tuned LLMs for Robust Harmful Content Mitigation
Lukas Edman, Daryna Dementieva, Alexander Fraser
Large language models (LLMs) demonstrate impressive performance across a wide range of general NLP tasks; however, their effectiveness in sensitive domains, such as hate speech det…
PluraMath: Extending Mathematical Reasoning Evaluation Beyond High-Resource Languages
Daryna Dementieva, Nikolay Babakov, Kathy Hämmerl +14
Mathematical reasoning has become a central task for evaluating and tuning reasoning Large Language Models (LLMs), yet existing benchmarks remain heavily biased toward high-resourc…