Comprehending and Reducing LLM Hallucinations

Author:

. Harsh,T Shobha

Abstract

The integration of large language models (LLM) into many artificial intelligence applications shows the best performance in tasks such as text mining, typing, question answering. Despite his success, his LL.M. The biggest concern is the emergence of so-called "hallucinations", especially in text-based systems and Q&As that rely on LL M. These hearings may lead to the spread of misinformation or fraud. This article explains the basics of AI illusions and highlights their importance in AI. Work involves deploying visualizations to a variety of tasks, including machine translation, surveys, interviews, content writing, LLM maps, and visualization questions. Additionally, this article explores potential strategies to reduce negative perceptions in order to increase the overall credibility of the LL.M.

Publisher

International Journal of Innovative Science and Research Technology

Reference54 articles.

1. V. Raunak, A. Menezes, M. Junczys-Dowmunt, The curious case of hallucinations in neural machine translation, in: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics, Online, 2021, pp. 1172–1183. URL: https://aclanthology.org/2021.naacl-main.92. doi:10.18653/v1/2021.naacl-main.92.

2. N. M. Guerreiro, D. Alves, J. Waldendorf, B. Haddow, A. Birch, P. Colombo, A. Martins, Hallucinations in large multilingual translation models, ArXiv abs/2303.16104 (2023). URL: https://api.semanticscholar.org/CorpusID:257771892.

3. D. Dale, E. Voita, J. Lam, P. Hansanti, C. Ropers, E. Kalbassi, C. Gao, L. Barrault, M. R. Costa-jussà, Halomi: A manually annotated benchmark for multilingual hallucination and omission detection in machine translation, ArXiv abs/2305.11746 (2023). URL: https://api.semanticscholar.org/CorpusID:258823059.

4. J. Pfeiffer, F. Piccinno, M. Nicosia, X. Wang, M. Reid, S. Ruder, mmt5: Modular multilingual pre-training solves source language hallucinations, ArXiv abs/2305.14224 (2023). URL: https://api.semanticscholar.org/CorpusID:258841429.

5. S. Lin, J. Hilton, O. Evans, TruthfulQA: Measuring how models mimic human falsehoods, in: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, Dublin, Ireland, 2022, pp. 3214–3252. URL: https://aclanthology.org/2022.acl-long.229. doi:10.18653/v1/2022. acl-long.229.

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