5 papers · 1 filter
Language Models Learn Universal Representations of Numbers and Here's Why You Should Care
Michal Štefánik, Timothee Mickus, Marek Kadlčík +7
Prior work has shown that large language models (LLMs) often converge to accurate input embedding for numbers, based on sinusoidal representations. In this work, we quantify that t…
Scaling Low-Resource MT via Synthetic Data Generation with LLMs
Ona de Gibert, Joseph Attieh, Teemu Vahtola +5
We investigate the potential of LLM-generated synthetic data for improving low-resource Machine Translation (MT). Focusing on seven diverse target languages, we construct a documen…
SemEval-2025 Task 3: Mu-SHROOM, the Multilingual Shared Task on Hallucinations and Related Observable Overgeneration Mistakes
Raúl Vázquez, Timothee Mickus, Elaine Zosa +15
We present the Mu-SHROOM shared task which is focused on detecting hallucinations and other overgeneration mistakes in the output of instruction-tuned large language models (LLMs).…
GlotEval: A Test Suite for Massively Multilingual Evaluation of Large Language Models
Hengyu Luo, Zihao Li, Joseph Attieh +12
Large language models (LLMs) are advancing at an unprecedented pace globally, with regions increasingly adopting these models for applications in their primary language. Evaluation…
Your Model is Overconfident, and Other Lies We Tell Ourselves
Timothee Mickus, Aman Sinha, Raúl Vázquez
The difficulty intrinsic to a given example, rooted in its inherent ambiguity, is a key yet often overlooked factor in evaluating neural NLP models. We investigate the interplay an…