Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Adilazuarda, Muhammad Farid"'
Autor:
Mukherjee, Sagnik, Adilazuarda, Muhammad Farid, Sitaram, Sunayana, Bali, Kalika, Aji, Alham Fikri, Choudhury, Monojit
Socio-demographic prompting is a commonly employed approach to study cultural biases in LLMs as well as for aligning models to certain cultures. In this paper, we systematically probe four LLMs (Llama 3, Mistral v0.2, GPT-3.5 Turbo and GPT-4) with pr
Externí odkaz:
http://arxiv.org/abs/2406.11661
Autor:
Lovenia, Holy, Mahendra, Rahmad, Akbar, Salsabil Maulana, Miranda, Lester James V., Santoso, Jennifer, Aco, Elyanah, Fadhilah, Akhdan, Mansurov, Jonibek, Imperial, Joseph Marvin, Kampman, Onno P., Moniz, Joel Ruben Antony, Habibi, Muhammad Ravi Shulthan, Hudi, Frederikus, Montalan, Railey, Ignatius, Ryan, Lopo, Joanito Agili, Nixon, William, Karlsson, Börje F., Jaya, James, Diandaru, Ryandito, Gao, Yuze, Amadeus, Patrick, Wang, Bin, Cruz, Jan Christian Blaise, Whitehouse, Chenxi, Parmonangan, Ivan Halim, Khelli, Maria, Zhang, Wenyu, Susanto, Lucky, Ryanda, Reynard Adha, Hermawan, Sonny Lazuardi, Velasco, Dan John, Kautsar, Muhammad Dehan Al, Hendria, Willy Fitra, Moslem, Yasmin, Flynn, Noah, Adilazuarda, Muhammad Farid, Li, Haochen, Lee, Johanes, Damanhuri, R., Sun, Shuo, Qorib, Muhammad Reza, Djanibekov, Amirbek, Leong, Wei Qi, Do, Quyet V., Muennighoff, Niklas, Pansuwan, Tanrada, Putra, Ilham Firdausi, Xu, Yan, Tai, Ngee Chia, Purwarianti, Ayu, Ruder, Sebastian, Tjhi, William, Limkonchotiwat, Peerat, Aji, Alham Fikri, Keh, Sedrick, Winata, Genta Indra, Zhang, Ruochen, Koto, Fajri, Yong, Zheng-Xin, Cahyawijaya, Samuel
Southeast Asia (SEA) is a region rich in linguistic diversity and cultural variety, with over 1,300 indigenous languages and a population of 671 million people. However, prevailing AI models suffer from a significant lack of representation of texts,
Externí odkaz:
http://arxiv.org/abs/2406.10118
Autor:
Zuhri, Zayd Muhammad Kawakibi, Adilazuarda, Muhammad Farid, Purwarianti, Ayu, Aji, Alham Fikri
Auto-regressive inference of transformers benefit greatly from Key-Value (KV) caching, but can lead to major memory bottlenecks as model size, batch size, and sequence length grow at scale. We introduce Multi-Layer Key-Value (MLKV) sharing, a novel a
Externí odkaz:
http://arxiv.org/abs/2406.09297
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:
Adilazuarda, Muhammad Farid, Mukherjee, Sagnik, Lavania, Pradhyumna, Singh, Siddhant, Aji, Alham Fikri, O'Neill, Jacki, Modi, Ashutosh, Choudhury, Monojit
We present a survey of more than 90 recent papers that aim to study cultural representation and inclusion in large language models (LLMs). We observe that none of the studies explicitly define "culture, which is a complex, multifaceted concept; inste
Externí odkaz:
http://arxiv.org/abs/2403.15412
Autor:
Adilazuarda, Muhammad Farid, Cahyawijaya, Samuel, Aji, Alham Fikri, Winata, Genta Indra, Purwarianti, Ayu
Pretrained language models (PLMs) have become remarkably adept at task and language generalization. Nonetheless, they often fail when faced with unseen languages. In this work, we present LinguAlchemy, a regularization method that incorporates variou
Externí odkaz:
http://arxiv.org/abs/2401.06034
Autor:
Adilazuarda, Muhammad Farid, Cahyawijaya, Samuel, Winata, Genta Indra, Fung, Pascale, Purwarianti, Ayu
Significant progress has been made on Indonesian NLP. Nevertheless, exploration of the code-mixing phenomenon in Indonesian is limited, despite many languages being frequently mixed with Indonesian in daily conversation. In this work, we explore code
Externí odkaz:
http://arxiv.org/abs/2311.12405
We expose the limitation of modular multilingual language models (MLMs) in multilingual inference scenarios with unknown languages. Existing evaluations of modular MLMs exclude the involvement of language identification (LID) modules, which obscures
Externí odkaz:
http://arxiv.org/abs/2311.12375
Autor:
Adilazuarda, Muhammad Farid
Significant progress has been made on text generation by pre-trained language models (PLMs), yet distinguishing between human and machine-generated text poses an escalating challenge. This paper offers an in-depth evaluation of three distinct methods
Externí odkaz:
http://arxiv.org/abs/2311.12373
Autor:
Cahyawijaya, Samuel, Lovenia, Holy, Aji, Alham Fikri, Winata, Genta Indra, Wilie, Bryan, Mahendra, Rahmad, Wibisono, Christian, Romadhony, Ade, Vincentio, Karissa, Koto, Fajri, Santoso, Jennifer, Moeljadi, David, Wirawan, Cahya, Hudi, Frederikus, Parmonangan, Ivan Halim, Alfina, Ika, Wicaksono, Muhammad Satrio, Putra, Ilham Firdausi, Rahmadani, Samsul, Oenang, Yulianti, Septiandri, Ali Akbar, Jaya, James, Dhole, Kaustubh D., Suryani, Arie Ardiyanti, Putri, Rifki Afina, Su, Dan, Stevens, Keith, Nityasya, Made Nindyatama, Adilazuarda, Muhammad Farid, Ignatius, Ryan, Diandaru, Ryandito, Yu, Tiezheng, Ghifari, Vito, Dai, Wenliang, Xu, Yan, Damapuspita, Dyah, Tho, Cuk, Karo, Ichwanul Muslim Karo, Fatyanosa, Tirana Noor, Ji, Ziwei, Fung, Pascale, Neubig, Graham, Baldwin, Timothy, Ruder, Sebastian, Sujaini, Herry, Sakti, Sakriani, Purwarianti, Ayu
We present NusaCrowd, a collaborative initiative to collect and unify existing resources for Indonesian languages, including opening access to previously non-public resources. Through this initiative, we have brought together 137 datasets and 118 sta
Externí odkaz:
http://arxiv.org/abs/2212.09648