Efficiency of question-answering pipeline in systems with combined LLM and knowledge base usage
dc.contributor.author | Androshchuk, Maksym | en_US |
dc.date.accessioned | 2025-01-31T06:50:33Z | |
dc.date.available | 2025-01-31T06:50:33Z | |
dc.date.issued | 2024 | |
dc.description.abstract | The 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.citation | Androshchuk 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.isbn | 978-617-555-228-5 | |
dc.identifier.uri | https://ekmair.ukma.edu.ua/handle/123456789/33389 | |
dc.language.iso | en | en_US |
dc.relation.source | XХXIX International Conference "Problems of decision making under uncertainties" (PDMU-2024), Brno, Czech Republic, September 9-10, 2024 : abstracts | en_US |
dc.status | first published | en_US |
dc.subject | integration of a large language model (LLM) | en_US |
dc.subject | KnowledgeNavigator framework | en_US |
dc.subject | learning capabilities of LLMs | en_US |
dc.subject | conference abstracts | en_US |
dc.title | Efficiency of question-answering pipeline in systems with combined LLM and knowledge base usage | en_US |
dc.type | Conference materials | en_US |
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