Mitigating Hallucinations in Large Language Models via Semantic Enrichment of Prompts: Insights from BioBERT and Ontological Integration
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A2UR9TZWT" target="_blank" >RIV/00216208:11320/25:2UR9TZWT - isvavai.cz</a>
Result on the web
<a href="https://aclanthology.org/2024.clib-1.30" target="_blank" >https://aclanthology.org/2024.clib-1.30</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Mitigating Hallucinations in Large Language Models via Semantic Enrichment of Prompts: Insights from BioBERT and Ontological Integration
Original language description
The advent of Large Language Models (LLMs) has been transformative for natural language processing, yet their tendency to produce “hallucinations”—outputs that are factually incorrect or entirely fabricated— remains a significant hurdle. This paper introduces a proactive methodology for reducing hallucinations by strategically enriching LLM prompts. This involves identifying key entities and contextual cues from varied domains and integrating this information into the LLM prompts to guide the model towards more accurate and relevant responses. Leveraging examples from BioBERT for biomedical entity recognition and ChEBI for chemical ontology, we illustrate a broader approach that encompasses semantic prompt enrichment as a versatile tool for enhancing LLM output accuracy. By examining the potential of semantic and ontological enrichment in diverse contexts, we aim to present a scalable strategy for improving the reliability of AI-generated content, thereby contributing to the ongoing efforts to refine LLMs for a wide range of applications.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the Sixth International Conference on Computational Linguistics in Bulgaria (CLIB 2024)
ISBN
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ISSN
2367-5578
e-ISSN
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Number of pages
5
Pages from-to
272-276
Publisher name
Department of Computational Linguistics, Institute for Bulgarian Language, Bulgarian Academy of Sciences
Place of publication
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Event location
Sofia, Bulgaria
Event date
Jan 1, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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