Zobrazeno 1 - 10
of 152
pro vyhledávání: '"Faisal Fahim"'
Autor:
Beenish Fatima Alam, Raima Bashir, Talha Nayab, Talib Hussain, Bilal Zaman Babar, Syed Hassan Jan, Faisal Fahim
Publikováno v:
BMC Oral Health, Vol 24, Iss 1, Pp 1-9 (2024)
Abstract Background Empathy is described as one’s ability to perceive and apprehend another person’s feelings, situation, emotions, and problems as their own. Empathetic behavior increases patients’ satisfaction, reduces discomfort, and helps w
Externí odkaz:
https://doaj.org/article/ff6d1c83d0464a05a104a9e40a271423
This report presents GMUNLP's participation to the Dialect-Copa shared task at VarDial 2024, which focuses on evaluating the commonsense reasoning capabilities of large language models (LLMs) on South Slavic micro-dialects. The task aims to assess ho
Externí odkaz:
http://arxiv.org/abs/2404.08092
The capacity and effectiveness of pre-trained multilingual models (MLMs) for zero-shot cross-lingual transfer is well established. However, phenomena of positive or negative transfer, and the effect of language choice still need to be fully understoo
Externí odkaz:
http://arxiv.org/abs/2403.20088
Autor:
Faisal, Fahim, Ahia, Orevaoghene, Srivastava, Aarohi, Ahuja, Kabir, Chiang, David, Tsvetkov, Yulia, Anastasopoulos, Antonios
Language technologies should be judged on their usefulness in real-world use cases. An often overlooked aspect in natural language processing (NLP) research and evaluation is language variation in the form of non-standard dialects or language varieti
Externí odkaz:
http://arxiv.org/abs/2403.11009
Choosing an appropriate tokenization scheme is often a bottleneck in low-resource cross-lingual transfer. To understand the downstream implications of text representation choices, we perform a comparative analysis on language models having diverse te
Externí odkaz:
http://arxiv.org/abs/2310.08078
Autor:
Faisal, Fahim
Modern NLP breakthrough includes large multilingual models capable of performing tasks across more than 100 languages. State-of-the-art language models came a long way, starting from the simple one-hot representation of words capable of performing ta
Externí odkaz:
http://arxiv.org/abs/2309.00949
Autor:
Shams, Fatin Abrar, Ratul, Rashed Hasan, Naf, Ahnaf Islam, Samir, Syed Shaek Hossain, Nishat, Mirza Muntasir, Faisal, Fahim, Hoque, Md. Ashraful
Superconductors have been among the most fascinating substances, as the fundamental concept of superconductivity as well as the correlation of critical temperature and superconductive materials have been the focus of extensive investigation since the
Externí odkaz:
http://arxiv.org/abs/2308.01932
Autor:
Song, Yueqi, Cui, Catherine, Khanuja, Simran, Liu, Pengfei, Faisal, Fahim, Ostapenko, Alissa, Winata, Genta Indra, Aji, Alham Fikri, Cahyawijaya, Samuel, Tsvetkov, Yulia, Anastasopoulos, Antonios, Neubig, Graham
Despite the major advances in NLP, significant disparities in NLP system performance across languages still exist. Arguably, these are due to uneven resource allocation and sub-optimal incentives to work on less resourced languages. To track and furt
Externí odkaz:
http://arxiv.org/abs/2305.14716
This report describes GMU's sentiment analysis system for the SemEval-2023 shared task AfriSenti-SemEval. We participated in all three sub-tasks: Monolingual, Multilingual, and Zero-Shot. Our approach uses models initialized with AfroXLMR-large, a pr
Externí odkaz:
http://arxiv.org/abs/2304.12979
Pretrained language models (PLMs) often fail to fairly represent target users from certain world regions because of the under-representation of those regions in training datasets. With recent PLMs trained on enormous data sources, quantifying their p
Externí odkaz:
http://arxiv.org/abs/2212.10408