Zobrazeno 1 - 10
of 109
pro vyhledávání: '"Winata, Genta Indra"'
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:
Anugraha, David, Winata, Genta Indra, Li, Chenyue, Irawan, Patrick Amadeus, Lee, En-Shiun Annie
Performance prediction is a method to estimate the performance of Language Models (LMs) on various Natural Language Processing (NLP) tasks, mitigating computational costs associated with model capacity and data for fine-tuning. Our paper introduces P
Externí odkaz:
http://arxiv.org/abs/2406.09334
Words have been represented in a high-dimensional vector space that encodes their semantic similarities, enabling downstream applications such as retrieving synonyms, antonyms, and relevant contexts. However, despite recent advances in multilingual l
Externí odkaz:
http://arxiv.org/abs/2406.07424
Autor:
Biderman, Stella, Schoelkopf, Hailey, Sutawika, Lintang, Gao, Leo, Tow, Jonathan, Abbasi, Baber, Aji, Alham Fikri, Ammanamanchi, Pawan Sasanka, Black, Sidney, Clive, Jordan, DiPofi, Anthony, Etxaniz, Julen, Fattori, Benjamin, Forde, Jessica Zosa, Foster, Charles, Hsu, Jeffrey, Jaiswal, Mimansa, Lee, Wilson Y., Li, Haonan, Lovering, Charles, Muennighoff, Niklas, Pavlick, Ellie, Phang, Jason, Skowron, Aviya, Tan, Samson, Tang, Xiangru, Wang, Kevin A., Winata, Genta Indra, Yvon, François, Zou, Andy
Effective evaluation of language models remains an open challenge in NLP. Researchers and engineers face methodological issues such as the sensitivity of models to evaluation setup, difficulty of proper comparisons across methods, and the lack of rep
Externí odkaz:
http://arxiv.org/abs/2405.14782
Autor:
Cahyawijaya, Samuel, Lovenia, Holy, Koto, Fajri, Putri, Rifki Afina, Dave, Emmanuel, Lee, Jhonson, Shadieq, Nuur, Cenggoro, Wawan, Akbar, Salsabil Maulana, Mahendra, Muhammad Ihza, Putri, Dea Annisayanti, Wilie, Bryan, Winata, Genta Indra, Aji, Alham Fikri, Purwarianti, Ayu, Fung, Pascale
Large language models (LLMs) show remarkable human-like capability in various domains and languages. However, a notable quality gap arises in low-resource languages, e.g., Indonesian indigenous languages, rendering them ineffective and inefficient in
Externí odkaz:
http://arxiv.org/abs/2404.06138
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
Autor:
Kautsar, Muhammad Dehan Al, Nurdini, Rahmah Khoirussyifa', Cahyawijaya, Samuel, Winata, Genta Indra, Purwarianti, Ayu
Task-oriented dialogue (ToD) systems have been mostly created for high-resource languages, such as English and Chinese. However, there is a need to develop ToD systems for other regional or local languages to broaden their ability to comprehend the d
Externí odkaz:
http://arxiv.org/abs/2311.00958
Autor:
Cahyawijaya, Samuel, Lovenia, Holy, Koto, Fajri, Adhista, Dea, Dave, Emmanuel, Oktavianti, Sarah, Akbar, Salsabil Maulana, Lee, Jhonson, Shadieq, Nuur, Cenggoro, Tjeng Wawan, Linuwih, Hanung Wahyuning, Wilie, Bryan, Muridan, Galih Pradipta, Winata, Genta Indra, Moeljadi, David, Aji, Alham Fikri, Purwarianti, Ayu, Fung, Pascale
Democratizing access to natural language processing (NLP) technology is crucial, especially for underrepresented and extremely low-resource languages. Previous research has focused on developing labeled and unlabeled corpora for these languages throu
Externí odkaz:
http://arxiv.org/abs/2309.10661
Transformer-based language models have achieved remarkable success in few-shot in-context learning and drawn a lot of research interest. However, these models' performance greatly depends on the choice of the example prompts and also has high variabi
Externí odkaz:
http://arxiv.org/abs/2306.10964