Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Gala, Jay"'
Autor:
Romero, David, Lyu, Chenyang, Wibowo, Haryo Akbarianto, Lynn, Teresa, Hamed, Injy, Kishore, Aditya Nanda, Mandal, Aishik, Dragonetti, Alina, Abzaliev, Artem, Tonja, Atnafu Lambebo, Balcha, Bontu Fufa, Whitehouse, Chenxi, Salamea, Christian, Velasco, Dan John, Adelani, David Ifeoluwa, Meur, David Le, Villa-Cueva, Emilio, Koto, Fajri, Farooqui, Fauzan, Belcavello, Frederico, Batnasan, Ganzorig, Vallejo, Gisela, Caulfield, Grainne, Ivetta, Guido, Song, Haiyue, Ademtew, Henok Biadglign, Maina, Hernán, Lovenia, Holy, Azime, Israel Abebe, Cruz, Jan Christian Blaise, Gala, Jay, Geng, Jiahui, Ortiz-Barajas, Jesus-German, Baek, Jinheon, Dunstan, Jocelyn, Alemany, Laura Alonso, Nagasinghe, Kumaranage Ravindu Yasas, Benotti, Luciana, D'Haro, Luis Fernando, Viridiano, Marcelo, Estecha-Garitagoitia, Marcos, Cabrera, Maria Camila Buitrago, Rodríguez-Cantelar, Mario, Jouitteau, Mélanie, Mihaylov, Mihail, Imam, Mohamed Fazli Mohamed, Adilazuarda, Muhammad Farid, Gochoo, Munkhjargal, Otgonbold, Munkh-Erdene, Etori, Naome, Niyomugisha, Olivier, Silva, Paula Mónica, Chitale, Pranjal, Dabre, Raj, Chevi, Rendi, Zhang, Ruochen, Diandaru, Ryandito, Cahyawijaya, Samuel, Góngora, Santiago, Jeong, Soyeong, Purkayastha, Sukannya, Kuribayashi, Tatsuki, Jayakumar, Thanmay, Torrent, Tiago Timponi, Ehsan, Toqeer, Araujo, Vladimir, Kementchedjhieva, Yova, Burzo, Zara, Lim, Zheng Wei, Yong, Zheng Xin, Ignat, Oana, Nwatu, Joan, Mihalcea, Rada, Solorio, Thamar, Aji, Alham Fikri
Visual Question Answering (VQA) is an important task in multimodal AI, and it is often used to test the ability of vision-language models to understand and reason on knowledge present in both visual and textual data. However, most of the current VQA
Externí odkaz:
http://arxiv.org/abs/2406.05967
Autor:
Chimoto, Everlyn Asiko, Gala, Jay, Ahia, Orevaoghene, Kreutzer, Julia, Bassett, Bruce A., Hooker, Sara
Neural Machine Translation models are extremely data and compute-hungry. However, not all data points contribute equally to model training and generalization. Data pruning to remove the low-value data points has the benefit of drastically reducing th
Externí odkaz:
http://arxiv.org/abs/2405.19462
The strength of modern large-scale neural networks lies in their ability to efficiently adapt to new tasks with few examples. Although extensive research has investigated the transferability of Vision Transformers (ViTs) to various downstream tasks u
Externí odkaz:
http://arxiv.org/abs/2403.10696
Autor:
Gala, Jay, Jayakumar, Thanmay, Husain, Jaavid Aktar, M, Aswanth Kumar, Khan, Mohammed Safi Ur Rahman, Kanojia, Diptesh, Puduppully, Ratish, Khapra, Mitesh M., Dabre, Raj, Murthy, Rudra, Kunchukuttan, Anoop
We announce the initial release of "Airavata," an instruction-tuned LLM for Hindi. Airavata was created by fine-tuning OpenHathi with diverse, instruction-tuning Hindi datasets to make it better suited for assistive tasks. Along with the model, we al
Externí odkaz:
http://arxiv.org/abs/2401.15006
RomanSetu: Efficiently unlocking multilingual capabilities of Large Language Models via Romanization
Autor:
Husain, Jaavid Aktar, Dabre, Raj, Kumar, Aswanth, Gala, Jay, Jayakumar, Thanmay, Puduppully, Ratish, Kunchukuttan, Anoop
This study addresses the challenge of extending Large Language Models (LLMs) to non-English languages that use non-Roman scripts. We propose an approach that utilizes the romanized form of text as an interface for LLMs, hypothesizing that its frequen
Externí odkaz:
http://arxiv.org/abs/2401.14280
Recent interest has surged in employing Large Language Models (LLMs) for machine translation (MT) via in-context learning (ICL) (Vilar et al., 2023). Most prior studies primarily focus on optimizing translation quality, with limited attention to unde
Externí odkaz:
http://arxiv.org/abs/2401.12097
Cloud analysis is a critical component of weather and climate science, impacting various sectors like disaster management. However, achieving fine-grained cloud analysis, such as cloud segmentation, in remote sensing remains challenging due to the in
Externí odkaz:
http://arxiv.org/abs/2311.05198
Autor:
Gala, Jay, Chitale, Pranjal A., AK, Raghavan, Gumma, Varun, Doddapaneni, Sumanth, Kumar, Aswanth, Nawale, Janki, Sujatha, Anupama, Puduppully, Ratish, Raghavan, Vivek, Kumar, Pratyush, Khapra, Mitesh M., Dabre, Raj, Kunchukuttan, Anoop
India has a rich linguistic landscape with languages from 4 major language families spoken by over a billion people. 22 of these languages are listed in the Constitution of India (referred to as scheduled languages) are the focus of this work. Given
Externí odkaz:
http://arxiv.org/abs/2305.16307
Hate speech detection has been the subject of high research attention, due to the scale of content created on social media. In spite of the attention and the sensitive nature of the task, privacy preservation in hate speech detection has remained und
Externí odkaz:
http://arxiv.org/abs/2302.09243
Autor:
Gala, Jay, Xie, Pengtao
Learning from mistakes is an effective learning approach widely used in human learning, where a learner pays greater focus on mistakes to circumvent them in the future to improve the overall learning outcomes. In this work, we aim to investigate how
Externí odkaz:
http://arxiv.org/abs/2112.00275