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pro vyhledávání: '"Kumar, Krtin"'
Question answering systems (QA) utilizing Large Language Models (LLMs) heavily depend on the retrieval component to provide them with domain-specific information and reduce the risk of generating inaccurate responses or hallucinations. Although the e
Externí odkaz:
http://arxiv.org/abs/2406.06458
Embedding matrices are key components in neural natural language processing (NLP) models that are responsible to provide numerical representations of input tokens.\footnote{In this paper words and subwords are referred to as \textit{tokens} and the t
Externí odkaz:
http://arxiv.org/abs/2104.08677
Word-embeddings are vital components of Natural Language Processing (NLP) models and have been extensively explored. However, they consume a lot of memory which poses a challenge for edge deployment. Embedding matrices, typically, contain most of the
Externí odkaz:
http://arxiv.org/abs/1910.06720
Autor:
Huang, Anqi, Li, Ruoping, Egorov, Vladimir, Tchouragoulov, Serguei, Kumar, Krtin, Makarov, Vadim
Publikováno v:
Phys. Rev. Applied 13, 034017 (2020)
Many quantum key distribution systems employ a laser followed by an optical attenuator to prepare weak coherent states in the source. Their mean photon number must be pre-calibrated to guarantee the security of key distribution. Here we experimentall
Externí odkaz:
http://arxiv.org/abs/1905.10795
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 36:10930-10937
Embedding matrices are key components in neural natural language processing (NLP) models that are responsible to provide numerical representations of input tokens.\footnote{In this paper words and subwords are referred to as \textit{tokens} and the t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3ee0a3fc20b99f40396aba3337c9dea7
https://doi.org/10.1609/aaai.v36i10.21340
https://doi.org/10.1609/aaai.v36i10.21340
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