5 papers
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…
Massively Multilingual Adaptation of Large Language Models Using Bilingual Translation Data
Shaoxiong Ji, Zihao Li, Jaakko Paavola +2
This paper investigates a critical design decision in the practice of massively multilingual continual pre-training -- the inclusion of parallel data. Specifically, we study the im…
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…
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…
Rethinking Multilingual Continual Pretraining: Data Mixing for Adapting LLMs Across Languages and Resources
Zihao Li, Shaoxiong Ji, Hengyu Luo +1
Large Language Models (LLMs) exhibit significant disparities in performance across languages, primarily benefiting high-resource languages while marginalizing underrepresented ones…