Zobrazeno 1 - 10
of 4 529
pro vyhledávání: '"Aremu, IN"'
Autor:
Aremu, Toluwani, Akinwehinmi, Oluwakemi, Nwagu, Chukwuemeka, Ahmed, Syed Ishtiaque, Orji, Rita, Del Amo, Pedro Arnau, Saddik, Abdulmotaleb El
We investigate and observe the behaviour and performance of Large Language Model (LLM)-backed chatbots in addressing misinformed prompts and questions with demographic information within the domains of Climate Change and Mental Health. Through a comb
Externí odkaz:
http://arxiv.org/abs/2410.10850
Large Language Models (LLMs) can be \emph{misused} to spread online spam and misinformation. Content watermarking deters misuse by hiding a message in model-generated outputs, enabling their detection using a secret watermarking key. Robustness is a
Externí odkaz:
http://arxiv.org/abs/2410.02440
Autor:
Tonja, Atnafu Lambebo, Dossou, Bonaventure F. P., Ojo, Jessica, Rajab, Jenalea, Thior, Fadel, Wairagala, Eric Peter, Aremu, Anuoluwapo, Moiloa, Pelonomi, Abbott, Jade, Marivate, Vukosi, Rosman, Benjamin
High-resource language models often fall short in the African context, where there is a critical need for models that are efficient, accessible, and locally relevant, even amidst significant computing and data constraints. This paper introduces Inkub
Externí odkaz:
http://arxiv.org/abs/2408.17024
Autor:
Kuwanto, Garry, Urua, Eno-Abasi E., Amuok, Priscilla Amondi, Muhammad, Shamsuddeen Hassan, Aremu, Anuoluwapo, Otiende, Verrah, Nanyanga, Loice Emma, Nyoike, Teresiah W., Akpan, Aniefon D., Udouboh, Nsima Ab, Archibong, Idongesit Udeme, Moses, Idara Effiong, Ige, Ifeoluwatayo A., Ajibade, Benjamin, Awokoya, Olumide Benjamin, Abdulmumin, Idris, Aliyu, Saminu Mohammad, Iro, Ruqayya Nasir, Ahmad, Ibrahim Said, Smith, Deontae, Michaels, Praise-EL, Adelani, David Ifeoluwa, Wijaya, Derry Tanti, Andy, Anietie
Publikováno v:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) 11349-11360
Low-resource languages often face challenges in acquiring high-quality language data due to the reliance on translation-based methods, which can introduce the translationese effect. This phenomenon results in translated sentences that lack fluency an
Externí odkaz:
http://arxiv.org/abs/2407.10152
Autor:
Ahia, Orevaoghene, Aremu, Anuoluwapo, Abagyan, Diana, Gonen, Hila, Adelani, David Ifeoluwa, Abolade, Daud, Smith, Noah A., Tsvetkov, Yulia
Yor\`ub\'a an African language with roughly 47 million speakers encompasses a continuum with several dialects. Recent efforts to develop NLP technologies for African languages have focused on their standard dialects, resulting in disparities for dial
Externí odkaz:
http://arxiv.org/abs/2406.19564
Autor:
Fares, Samar, Ziu, Klea, Aremu, Toluwani, Durasov, Nikita, Takáč, Martin, Fua, Pascal, Nandakumar, Karthik, Laptev, Ivan
Vision-Language Models (VLMs) are becoming increasingly vulnerable to adversarial attacks as various novel attack strategies are being proposed against these models. While existing defenses excel in unimodal contexts, they currently fall short in saf
Externí odkaz:
http://arxiv.org/abs/2406.09250
Federated learning (FL) has emerged as a pivotal approach in machine learning, enabling multiple participants to collaboratively train a global model without sharing raw data. While FL finds applications in various domains such as healthcare and fina
Externí odkaz:
http://arxiv.org/abs/2406.00569
Nigeria is a multilingual country with 500+ languages. Naija is a Nigerian-Pidgin spoken by approx. 120M speakers in Nigeria and it is a mixed language (e.g., English, Portuguese, Yoruba, Hausa and Igbo). Although it has mainly been a spoken language
Externí odkaz:
http://arxiv.org/abs/2404.19442
Autor:
Aremu, Toluwani
Private computation of nonlinear functions, such as Rectified Linear Units (ReLUs) and max-pooling operations, in deep neural networks (DNNs) poses significant challenges in terms of storage, bandwidth, and time consumption. To address these challeng
Externí odkaz:
http://arxiv.org/abs/2312.15229
Autor:
Wang, Jiayi, Adelani, David Ifeoluwa, Agrawal, Sweta, Masiak, Marek, Rei, Ricardo, Briakou, Eleftheria, Carpuat, Marine, He, Xuanli, Bourhim, Sofia, Bukula, Andiswa, Mohamed, Muhidin, Olatoye, Temitayo, Adewumi, Tosin, Mokayed, Hamam, Mwase, Christine, Kimotho, Wangui, Yuehgoh, Foutse, Aremu, Anuoluwapo, Ojo, Jessica, Muhammad, Shamsuddeen Hassan, Osei, Salomey, Omotayo, Abdul-Hakeem, Chukwuneke, Chiamaka, Ogayo, Perez, Hourrane, Oumaima, Anigri, Salma El, Ndolela, Lolwethu, Mangwana, Thabiso, Mohamed, Shafie Abdi, Hassan, Ayinde, Awoyomi, Oluwabusayo Olufunke, Alkhaled, Lama, Al-Azzawi, Sana, Etori, Naome A., Ochieng, Millicent, Siro, Clemencia, Njoroge, Samuel, Muchiri, Eric, Kimotho, Wangari, Momo, Lyse Naomi Wamba, Abolade, Daud, Ajao, Simbiat, Shode, Iyanuoluwa, Macharm, Ricky, Iro, Ruqayya Nasir, Abdullahi, Saheed S., Moore, Stephen E., Opoku, Bernard, Akinjobi, Zainab, Afolabi, Abeeb, Obiefuna, Nnaemeka, Ogbu, Onyekachi Raphael, Brian, Sam, Otiende, Verrah Akinyi, Mbonu, Chinedu Emmanuel, Sari, Sakayo Toadoum, Lu, Yao, Stenetorp, Pontus
Despite the recent progress on scaling multilingual machine translation (MT) to several under-resourced African languages, accurately measuring this progress remains challenging, since evaluation is often performed on n-gram matching metrics such as
Externí odkaz:
http://arxiv.org/abs/2311.09828