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of 7
pro vyhledávání: '"Mousi, Basel"'
Autor:
Mousi, Basel, Durrani, Nadir, Ahmad, Fatema, Hasan, Md. Arid, Hasanain, Maram, Kabbani, Tameem, Dalvi, Fahim, Chowdhury, Shammur Absar, Alam, Firoj
Arabic, with its rich diversity of dialects, remains significantly underrepresented in Large Language Models, particularly in dialectal variations. We address this gap by introducing seven synthetic datasets in dialects alongside Modern Standard Arab
Externí odkaz:
http://arxiv.org/abs/2409.11404
Despite their remarkable ability to capture linguistic nuances across diverse languages, questions persist regarding the degree of alignment between languages in multilingual embeddings. Drawing inspiration from research on high-dimensional represent
Externí odkaz:
http://arxiv.org/abs/2405.14535
Autor:
Dalvi, Fahim, Hasanain, Maram, Boughorbel, Sabri, Mousi, Basel, Abdaljalil, Samir, Nazar, Nizi, Abdelali, Ahmed, Chowdhury, Shammur Absar, Mubarak, Hamdy, Ali, Ahmed, Hawasly, Majd, Durrani, Nadir, Alam, Firoj
The recent development and success of Large Language Models (LLMs) necessitate an evaluation of their performance across diverse NLP tasks in different languages. Although several frameworks have been developed and made publicly available, their cust
Externí odkaz:
http://arxiv.org/abs/2308.04945
Autor:
Abdelali, Ahmed, Mubarak, Hamdy, Chowdhury, Shammur Absar, Hasanain, Maram, Mousi, Basel, Boughorbel, Sabri, Kheir, Yassine El, Izham, Daniel, Dalvi, Fahim, Hawasly, Majd, Nazar, Nizi, Elshahawy, Yousseif, Ali, Ahmed, Durrani, Nadir, Milic-Frayling, Natasa, Alam, Firoj
Recent advancements in Large Language Models (LLMs) have significantly influenced the landscape of language and speech research. Despite this progress, these models lack specific benchmarking against state-of-the-art (SOTA) models tailored to particu
Externí odkaz:
http://arxiv.org/abs/2305.14982
Work done to uncover the knowledge encoded within pre-trained language models rely on annotated corpora or human-in-the-loop methods. However, these approaches are limited in terms of scalability and the scope of interpretation. We propose using a la
Externí odkaz:
http://arxiv.org/abs/2305.13386
Publikováno v:
Artificial Intelligence Applications and Innovations. AIAI 2022. IFIP Advances in Information and Communication Technology, vol 647. Springer, Cham
Current Explainable AI (ExAI) methods, especially in the NLP field, are conducted on various datasets by employing different metrics to evaluate several aspects. The lack of a common evaluation framework is hindering the progress tracking of such met
Externí odkaz:
http://arxiv.org/abs/2210.06916
Autor:
Abdelali, Ahmed, Mubarak, Hamdy, Chowdhury, Shammur Absar, Hasanain, Maram, Mousi, Basel, Boughorbel, Sabri, Kheir, Yassine El, Izham, Daniel, Dalvi, Fahim, Hawasly, Majd, Nazar, Nizi, Elshahawy, Yousseif, Ali, Ahmed, Durrani, Nadir, Milic-Frayling, Natasa, Alam, Firoj
With large Foundation Models (FMs), language technologies (AI in general) are entering a new paradigm: eliminating the need for developing large-scale task-specific datasets and supporting a variety of tasks through set-ups ranging from zero-shot to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c91f80c3410b73015f1cb8b3587109e1