14 citations · 27 across the 35 of their papers we have counts for
8 papers · 1 filter
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…
MatheMagic: Generating Dynamic Mathematics Benchmarks Robust to Memorization
Dayyán O'Brien, Barry Haddow, Emily Allaway +1
Conducting contamination-free evaluation of mathematical capabilities can be difficult for two reasons: models may memorize a test set once it is made public, and current mathemati…
DocHPLT: A Massively Multilingual Document-Level Translation Dataset
Dayyán O'Brien, Bhavitvya Malik, Ona de Gibert +3
Existing document-level machine translation resources are only available for a handful of languages, mostly high-resourced ones. To facilitate the training and evaluation of docume…
A3D-MoE: Acceleration of Large Language Models with Mixture of Experts via 3D Heterogeneous Integration
Wei-Hsing Huang, Janak Sharda, Cheng-Jhih Shih +6
Conventional large language models (LLMs) are equipped with dozens of GB to TB of model parameters, making inference highly energy-intensive and costly as all the weights need to b…
CHARM: Calibrating Reward Models With Chatbot Arena Scores
Xiao Zhu, Chenmien Tan, Pinzhen Chen +4
Reward models (RMs) play a crucial role in Reinforcement Learning from Human Feedback by serving as proxies for human preferences in aligning large language models. However, they s…
XL-Suite: Cross-Lingual Synthetic Training and Evaluation Data for Open-Ended Generation
Vivek Iyer, Pinzhen Chen, Ricardo Rei +1
Cross-lingual open-ended generation - responding in a language different from that of the query - is an important yet understudied problem. This work proposes XL-Instruct, a novel…
An Expanded Massive Multilingual Dataset for High-Performance Language Technologies (HPLT)
Laurie Burchell, Ona de Gibert, Nikolay Arefyev +32
Training state-of-the-art large language models requires vast amounts of clean and diverse textual data. However, building suitable multilingual datasets remains a challenge. In th…
Generalizing From Short to Long: Effective Data Synthesis for Long-Context Instruction Tuning
Wenhao Zhu, Pinzhen Chen, Hanxu Hu +4
Long-context modelling for large language models (LLMs) has been a key area of recent research because many real world use cases require reasoning over longer inputs such as docume…