Zobrazeno 1 - 10
of 3 637
pro vyhledávání: '"P., Koehn"'
Autor:
Wang, Longyue, Liu, Siyou, Lyu, Chenyang, Jiao, Wenxiang, Wang, Xing, Xu, Jiahao, Tu, Zhaopeng, Gu, Yan, Chen, Weiyu, Wu, Minghao, Zhou, Liting, Koehn, Philipp, Way, Andy, Yuan, Yulin
Following last year, we have continued to host the WMT translation shared task this year, the second edition of the Discourse-Level Literary Translation. We focus on three language directions: Chinese-English, Chinese-German, and Chinese-Russian, wit
Externí odkaz:
http://arxiv.org/abs/2412.11732
Autor:
Mondal, Sourav, Netz, Julia, Hunger, David, Suhr, Simon, Sarkar, Biprajit, van Slageren, Joris, Köhn, Andreas, Lunghi, Alessandro
Magnetic relaxation in coordination compounds is largely dominated by the interaction of the spin with phonons. Large zero-field splitting and exchange coupling values have been empirically found to strongly suppress spin relaxation and have been use
Externí odkaz:
http://arxiv.org/abs/2412.04362
Large language models (LLMs) have achieved remarkable success across various NLP tasks, yet their focus has predominantly been on English due to English-centric pre-training and limited multilingual data. While some multilingual LLMs claim to support
Externí odkaz:
http://arxiv.org/abs/2410.03115
Autor:
Koehn, Hauke, Giangrandi, Edoardo, Kunert, Nina, Somasundaram, Rahul, Sagun, Violetta, Dietrich, Tim
If dark matter (DM) accumulates inside neutron stars (NS), it changes their internal structure and causes a shift of the tidal deformability from the value predicted by the dense-matter equation of state (EOS). In principle, this shift could be obser
Externí odkaz:
http://arxiv.org/abs/2408.14711
Autor:
Kocmi, Tom, Avramidis, Eleftherios, Bawden, Rachel, Bojar, Ondrej, Dvorkovich, Anton, Federmann, Christian, Fishel, Mark, Freitag, Markus, Gowda, Thamme, Grundkiewicz, Roman, Haddow, Barry, Karpinska, Marzena, Koehn, Philipp, Marie, Benjamin, Murray, Kenton, Nagata, Masaaki, Popel, Martin, Popovic, Maja, Shmatova, Mariya, Steingrímsson, Steinþór, Zouhar, Vilém
This is the preliminary ranking of WMT24 General MT systems based on automatic metrics. The official ranking will be a human evaluation, which is superior to the automatic ranking and supersedes it. The purpose of this report is not to interpret any
Externí odkaz:
http://arxiv.org/abs/2407.19884
Autor:
Koehn, Hauke, Wouters, Thibeau, Rose, Henrik, Pang, Peter T. H., Somasundaram, Rahul, Tews, Ingo, Dietrich, Tim
Nuclear theory and experiments, alongside astrophysical observations, constrain the equation of state (EOS) of supranuclear-dense matter. Conversely, knowledge of the EOS allows an improved interpretation of nuclear or astrophysical data. In this art
Externí odkaz:
http://arxiv.org/abs/2407.07837
Autor:
Lu, Taiming, Koehn, Philipp
This paper investigates the propagation of harmful information in multilingual large language models (LLMs) and evaluates the efficacy of various unlearning methods. We demonstrate that fake information, regardless of the language it is in, once intr
Externí odkaz:
http://arxiv.org/abs/2406.13748
Data availability limits the scope of any given task. In machine translation, historical models were incapable of handling longer contexts, so the lack of document-level datasets was less noticeable. Now, despite the emergence of long-sequence method
Externí odkaz:
http://arxiv.org/abs/2406.03869
Autor:
Wu, John F., Hyk, Alina, McCormick, Kiera, Ye, Christine, Astarita, Simone, Baral, Elina, Ciuca, Jo, Cranney, Jesse, Field, Anjalie, Iyer, Kartheik, Koehn, Philipp, Kotler, Jenn, Kruk, Sandor, Ntampaka, Michelle, O'Neill, Charles, Peek, Joshua E. G., Sharma, Sanjib, Yunus, Mikaeel
Large Language Models (LLMs) are shifting how scientific research is done. It is imperative to understand how researchers interact with these models and how scientific sub-communities like astronomy might benefit from them. However, there is currentl
Externí odkaz:
http://arxiv.org/abs/2405.20389
Autor:
Myklebust, Even Moa, Frigessi, Arnoldo, Schjesvold, Fredrik, Foo, Jasmine, Leder, Kevin, Köhn-Luque, Alvaro
Predicting cancer dynamics under treatment is challenging due to high inter-patient heterogeneity, lack of predictive biomarkers, and sparse and noisy longitudinal data. Mathematical models can summarize cancer dynamics by a few interpretable paramet
Externí odkaz:
http://arxiv.org/abs/2405.14508