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pro vyhledávání: '"Koshiyama, Adriano Soares"'
Recent advancements in Large Language Models (LLMs) have significantly increased their presence in human-facing Artificial Intelligence (AI) applications. However, LLMs could reproduce and even exacerbate stereotypical outputs from training data. Thi
Externí odkaz:
http://arxiv.org/abs/2404.01768
Large Language Models (LLMs) are increasingly being utilized by both candidates and employers in the recruitment context. However, with this comes numerous ethical concerns, particularly related to the lack of transparency in these "black-box" models
Externí odkaz:
http://arxiv.org/abs/2402.08341
Large Language Models (LLM) have made significant advances in the recent past becoming more mainstream in Artificial Intelligence (AI) enabled human-facing applications. However, LLMs often generate stereotypical output inherited from historical data
Externí odkaz:
http://arxiv.org/abs/2311.14126
Publikováno v:
In Computers and Education: Artificial Intelligence December 2024 7
Derivative traders are usually required to scan through hundreds, even thousands of possible trades on a daily basis. Up to now, not a single solution is available to aid in their job. Hence, this work aims to develop a trading recommendation system,
Externí odkaz:
http://arxiv.org/abs/1810.02125
Autor:
Kazim, Emre, Koshiyama, Adriano Soares
Publikováno v:
In Patterns 10 September 2021 2(9)
Fragmentation of production across national boundaries results in global supply chains. To govern effectively in this type of economy requires a combination of public data with private initiatives (from business, banks and NGOs). But basically, there
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::80d74bcfd7897c466369ec68d33f2b53
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