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pro vyhledávání: '"Shehata, Dahlia"'
Conversational prompt-engineering-based large language models (LLMs) have enabled targeted control over the output creation, enhancing versatility, adaptability and adhoc retrieval. From another perspective, digital misinformation has reached alarmin
Externí odkaz:
http://arxiv.org/abs/2404.16859
Autor:
Shehata, Dahlia
Despite the advantages of their low-resource settings, traditional sparse retrievers depend on exact matching approaches between high-dimensional bag-of-words (BoW) representations of both the queries and the collection. As a result, retrieval perfor
Externí odkaz:
http://arxiv.org/abs/2404.08678
Despite the advantages of their low-resource settings, traditional sparse retrievers depend on exact matching approaches between high-dimensional bag-of-words (BoW) representations of both the queries and the collection. As a result, retrieval perfor
Externí odkaz:
http://arxiv.org/abs/2208.04887
Caseworkers are trained to write detailed narratives about families in Child-Welfare (CW) which informs collaborative high-stakes decision-making. Unlike other administrative data, these narratives offer a more credible source of information with res
Externí odkaz:
http://arxiv.org/abs/2203.05169
Autor:
Shehata, Dahlia, Leitmeir, Florian
Publikováno v:
Antike Welt, 2020 Jan 01(1), 8-12.
Externí odkaz:
https://www.jstor.org/stable/26918513
Autor:
Shehata, Dahlia.
Bewerking van diss. Georg-August-Universität, 2004.
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Met lit.opgave en indices.