Fetching the paper…
Reading the bibliography…
We introduce Jais and Jais-chat, new state-of-the-art Arabic-centric foundation and instruction-tuned open generative large language models (LLMs).
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
Earlier work this paper cites.
The Enron corpus: A new dataset for email classification research
Bryan Klimt and Yiming Yang · 2004
Earlier work this paper cites.
Europarl: A parallel corpus for statistical machine translation
Philipp Koehn · 2005
Earlier work this paper cites.
Hybridity in MT: Experiments on the Europarl corpus
Declan Groves and Andy Way · 2006
Earlier work this paper cites.
ANERsys: An Arabic named entity recognition system based on maximum entropy
Yassine Benajiba, Paolo Rosso, and José Miguel BenedíRuiz · 2007
Earlier work this paper cites.
Source language markers in europarl translations
Hans Van Halteren · 2008
Earlier work this paper cites.
Building large monolingual dictionaries at the Leipzig corpora collection: From 100 to 200 languages
Dirk Goldhahn, Thomas Eckart, and Uwe Quasthoff · 2012
Earlier work this paper cites.
Parallel data, tools and interfaces in OPUS
Jörg Tiedemann · 2012
Earlier work this paper cites.
More effective boilerplate removal-the GoldMiner algorithm
István Endrédy and Attila Novák · 2013
Earlier work this paper cites.
Abu El-Khair Corpus: A modern standard Arabic corpus
Ibrahim Abu El-Khair · 2016
Earlier work this paper cites.
Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch · 2016
Earlier work this paper cites.
The United Nations parallel corpus v1.0
Michał Ziemski, Marcin Junczys-Dowmunt, and Bruno Pouliquen · 2016
Earlier work this paper cites.
Towards a map of the syntactic similarity of languages
Alina Maria Ciobanu, Liviu P Dinu, and Andrea Sgarro · 2017
Earlier work this paper cites.
Searching for activation functions
Prajit Ramachandran, Barret Zoph, and Quoc V. Le · 2017
Earlier work this paper cites.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2018
Earlier work this paper cites.
SemEval-2018 task 1: Affect in tweets
Saif Mohammad, Felipe Bravo-Marquez, Mohammad Salameh, and Svetlana Kiritchenko · 2018
Earlier work this paper cites.
Can a suit of armor conduct electricity? A new dataset for open book question answering
Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal · 2018
Cited alongside, same era.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever · 2018
Cited alongside, same era.
Cross-lingual language model pretraining
Alexis Conneau and Guillaume Lample · 2019
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Cited alongside, same era.
OpenWebTextCorpus
Aaron Gokaslan and Vanya Cohen · 2019
Cited alongside, same era.
Analysing mathematical reasoning abilities of neural models
David Saxton, Edward Grefenstette, Felix Hill, and Pushmeet Kohli · 2019
Cited alongside, same era.
SituatedQA: Incorporating extra-linguistic contexts into QA
Michael Zhang and Eunsol Choi · 2021
Later among the works it cites.
TruthfulQA: Measuring how models mimic human falsehoods
Stephanie Lin, Jacob Hilton, and Owain Evans · 2022
Later among the works it cites.
AraT5: Text-to-text transformers for Arabic language generation
El Moatez Billah Nagoudi, AbdelRahim Elmadany, and Muhammad Abdul-Mageed · 2022
Later among the works it cites.
Train short, test long: Attention with linear biases enables input length extrapolation
Ofir Press, Noah Smith, and Mike Lewis · 2022
Later among the works it cites.
Transformer-based architecture for empathy prediction and emotion classification
Himil Vasava, Pramegh Uikey, Gaurav Wasnik, and Raksha Sharma · 2022
Later among the works it cites.
Unnatural Instructions: Tuning language models with (almost) no human labor
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
AraBERT: Transformer-based model for Arabic language understanding
Wissam Antoun, Fady Baly, and Hazem Hajj · 2020
Cited alongside, same era.
ELECTRA: pre-training text encoders as discriminators rather than generators
Kevin Clark, Minh-Thang Luong, Quoc V. Le, and Christopher D. Manning · 2020
Cited alongside, same era.
An empirical study of pre-trained transformers for Arabic information extraction
Wuwei Lan, Yang Chen, Wei Xu, and Alan Ritter · 2020
Cited alongside, same era.
CrowS-pairs: A challenge dataset for measuring social biases in masked language models
Nikita Nangia, Clara Vania, Rasika Bhalerao, and Samuel R. Bowman · 2020
Cited alongside, same era.
KUISAIL at SemEval-2020 task 12: BERT-CNN for offensive speech identification in social media
Ali Safaya, Moutasem Abdullatif, and Deniz Yuret · 2020
Cited alongside, same era.
GLU variants improve transformer
Noam Shazeer · 2020
Cited alongside, same era.
Or Honovich, Thomas Scialom, Omer Levy, and Timo Schick · 2023
Closest in time.
Tokenization impacts multilingual language modeling: Assessing vocabulary allocation and overlap across languages
Tomasz Limisiewicz, Jiří Balhar, and David Mareček · 2023
Closest in time.
ChatGPT as a factual inconsistency evaluator for abstractive text summarization
Zheheng Luo, Qianqian Xie, and Sophia Ananiadou · 2023
Closest in time.
OpenAI · 2023
Closest in time.
Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, and Jianfeng Gao · 2023
Closest in time.
Language model tokenizers introduce unfairness between languages
Aleksandar Petrov, Emanuele La Malfa, Philip HS Torr, and Adel Bibi · 2023
Closest in time.
Alpaca-CoT: An instruction fine-tuning platform with instruction data collection and unified large language models interface
Zheng Lin Qingyi Si · 2023
Closest in time.
Petter Törnberg · 2023
Closest in time.
Style Over Substance: Evaluation biases for large language models
Minghao Wu and Alham Fikri Aji · 2023
Closest in time.
Privacy- and utility-preserving NLP with anonymized data: A case study of pseudonymization
Oleksandr Yermilov, Vipul Raheja, and Artem Chernodub · 2023
Closest in time.