13 papers
Can Humans Dream of Electric Sheep? Human-Written Samples for Fine-Grained Vision-and-Language Hallucination Benchmarking
Timothee Mickus, Claudio Savelli, Eduardo Calò +10
In an age of rapid model turnover, how do we make hallucination evaluation more perennial? We explore whether human-written hallucination samples could take the place of model-gene…
MultiSynt/MT: Trillion-Token Multi-Parallel Pre-Training Data Translated Across 36 Languages
Maximilian Idahl, Jörg Tiedemann, Sampo Pyysalo +19
Open web-scale pre-training corpora remain concentrated in English, limiting multilingual LLM development. We introduce MultiSynt/MT, an open synthetic parallel corpus with approxi…
Model-Based Quality Assessment for Massively Multilingual Parallel Data
Abdelaziz M. A. Ibrahim, Zihao Li, Jörg Tiedemann +1
Large-scale multilingual bitext often contains two distinct problems: non-parallel sentence pairs and low-quality translations. We decompose model-based assessment for such data in…
The Impact of Vocabulary Overlaps on Knowledge Transfer in Multilingual Machine Translation
Oona Itkonen, Jörg Tiedemann
Knowledge transfer, especially across related languages, has been found beneficial for multilingual neural machine translation (MNMT), but some aspects are still under-explored and…
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…
Test-Time Scaling of Reasoning Models for Machine Translation
Zihao Li, Shaoxiong Ji, Jörg Tiedemann
Test-time scaling (TTS) has enhanced the performance of Reasoning Models (RMs) on various tasks such as math and coding, yet its efficacy in machine translation (MT) remains undere…