Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Bajaj, Manav M.; Senthil Vadivu, M.; Jeevanand, E.S. Title A name given to the resource A Hybrid Framework for Detecting Hallucinations in Large Language Model Outputs Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 4th International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2025;pp.1028-1034 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICAAIC64647.2025.11330957" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICAAIC64647.2025.11330957</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105034877027?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034877027?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Bajaj M.M., Christ (Deemed to be University), Bangalore, India; Senthil Vadivu M., Christ (Deemed to be University), Dept. of Statistics and Data Science, Bangalore, India; Jeevanand E.S., Christ (Deemed to be University), Dept. of Statistics and Data Science, Bangalore, India Description An account of the resource With Large Language Models (LLMs) continuously growing, they are on a path to replace the search engines soon. No matter how powerful they get, there is a certain level of uncertainty because they tend to hallucinate. Hallucination here refers to generate factually incorrect data, this can include making up names, generating false links and fabricating stories. This makes it extremely difficult to trust large language models. Existing papers provide solutions which are either not monetarily feasible or lack capabilities to build a robust hallucination detector. This paper aims to build a low resource hallucination detector which combines multiple heuristic signals like semantic similarity, self-consistency, external grounding via Wikipedia, NER overlap, flexible numerical check and a quantized LLM Falcon-7b. This eliminates the need to train the model from scratch. Upon evaluating with an input dataset of 50 questions the model was able to achieve an accuracy of 88%. 2025 IEEE. Subject The topic of the resource Hallucination Detection; Heuristic Analysis; Large Language Models (LLMs); Low-Resource AI; Self-Consistency Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833156587-9; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online