A Hybrid Framework for Detecting Hallucinations in Large Language Model Outputs
Hallucination Detection; Heuristic Analysis; Large Language Models (LLMs); Low-Resource AI; Self-Consistency
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.
Bajaj, Manav M.; Senthil Vadivu, M.; Jeevanand, E.S.
4th International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2025;pp.1028-1034
Institute of Electrical and Electronics Engineers Inc.
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/ICAAIC64647.2025.11330957 https://www.scopus.com/pages/publications/105034877027?origin=resultslist
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