4 citations · 9 across the 6 of their papers we have counts for
9 papers · 1 filter
Domain Adaptation of Foundation LLMs for e-Commerce
Christian Herold, Michael Kozielski, Tala Bazazo +6
We present the e-Llama models: 8 billion and 70 billion parameter large language models that are adapted towards the e-commerce domain. These models are meant as foundation models…
LiLiuM: eBay's Large Language Models for e-commerce
Christian Herold, Michael Kozielski, Leonid Ekimov +3
We introduce the LiLiuM series of large language models (LLMs): 1B, 7B, and 13B parameter models developed 100% in-house to fit eBay's specific needs in the e-commerce domain. This…
Document-Level Language Models for Machine Translation
Frithjof Petrick, Christian Herold, Pavel Petrushkov +2
Despite the known limitations, most machine translation systems today still operate on the sentence-level. One reason for this is, that most parallel training data is only sentence…
Towards Reinforcement Learning for Pivot-based Neural Machine Translation with Non-autoregressive Transformer
Evgeniia Tokarchuk, Jan Rosendahl, Weiyue Wang +4
Pivot-based neural machine translation (NMT) is commonly used in low-resource setups, especially for translation between non-English language pairs. It benefits from using high res…
Integrated Training for Sequence-to-Sequence Models Using Non-Autoregressive Transformer
Evgeniia Tokarchuk, Jan Rosendahl, Weiyue Wang +4
Complex natural language applications such as speech translation or pivot translation traditionally rely on cascaded models. However, cascaded models are known to be prone to error…
Pivot-based Transfer Learning for Neural Machine Translation between Non-English Languages
Yunsu Kim, Petre Petrov, Pavel Petrushkov +2
We present effective pre-training strategies for neural machine translation (NMT) using parallel corpora involving a pivot language, i.e., source-pivot and pivot-target, leading to…