Efficiency of question-answering pipeline in systems with combined LLM and knowledge base usage

dc.contributor.authorAndroshchuk, Maksymen_US
dc.date.accessioned2025-01-31T06:50:33Z
dc.date.available2025-01-31T06:50:33Z
dc.date.issued2024
dc.description.abstractThe integration of a large language model (LLM) with a knowledge base (KB) has been shown to significantly enhance the efficiency of question-answering (QA) pipelines. The KnowledgeNavigator framework has demonstrated effectiveness and generalization in QA tasks [1].en_US
dc.identifier.citationAndroshchuk M. Efficiency of question-answering pipeline in systems with combined LLM and knowledge base usage / M. Androshchuk // XХXIX International Conference "Problems of decision making under uncertainties" (PDMU-2024), Brno, Czech Republic, September 9-10, 2024 : abstracts / Taras Shevchenko National University of Kyiv (Ukraine), University of Defence, Brno, Czech Republic [et al.]. - Кyiv, 2024. - P. 17.en_US
dc.identifier.isbn978-617-555-228-5
dc.identifier.urihttps://ekmair.ukma.edu.ua/handle/123456789/33389
dc.language.isoenen_US
dc.relation.sourceXХXIX International Conference "Problems of decision making under uncertainties" (PDMU-2024), Brno, Czech Republic, September 9-10, 2024 : abstractsen_US
dc.statusfirst publisheden_US
dc.subjectintegration of a large language model (LLM)en_US
dc.subjectKnowledgeNavigator frameworken_US
dc.subjectlearning capabilities of LLMsen_US
dc.subjectconference abstractsen_US
dc.titleEfficiency of question-answering pipeline in systems with combined LLM and knowledge base usageen_US
dc.typeConference materialsen_US
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