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pro vyhledávání: '"Anuyah, Oghenemaro"'
Autor:
Szymanski, Annalisa, Gebreegziabher, Simret Araya, Anuyah, Oghenemaro, Metoyer, Ronald A., Li, Toby Jia-Jun
Large Language Models (LLMs) are increasingly utilized for domain-specific tasks, yet integrating domain expertise into evaluating their outputs remains challenging. A common approach to evaluating LLMs is to use metrics, or criteria, which are asser
Externí odkaz:
http://arxiv.org/abs/2410.02054
Recent breakthroughs in deep-learning (DL) approaches have resulted in the dynamic generation of trace links that are far more accurate than was previously possible. However, DL-generated links lack clear explanations, and therefore non-experts in th
Externí odkaz:
http://arxiv.org/abs/2204.11914
Recommender algorithms performance is often associated with the availability of sufficient historical rating data. Unfortunately, when it comes to children, this data is seldom available. In this paper, we report on an initial analysis conducted to e
Externí odkaz:
http://arxiv.org/abs/1808.08274
Publikováno v:
Aslib Journal of Information Management, 2020, Vol. 72, Issue 1, pp. 88-111.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/AJIM-06-2019-0143
Explanations can help users of Artificial Intelligent (AI) systems gain a better understanding of the reasoning behind the model’s decision, facilitate their trust in AI, and assist them in making informed decisions. Due to its numerous benefits in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f14de54fc966a5baf25290e3ffde6b3f
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