ISSN2320-2882
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Paper Title
Hallucination in Large Language Models: A Comprehensive Survey of Types, Detection Methods, Mitigation Strategies, and Future Directions
Authors
1. Jiya Patel , 2. Manthan Khopkar
Keywords
Large Language Models, Hallucination, Natural Language Generation, Factual Accuracy, Retrieval-Augmented Generation, Transformer Models, Misinformation, Fact Verification, RLHF, Chain-of-Thought Prompting, TruthfulQA, HaluEval, Self-Consistency, Knowledge Grounding, Trustworthy AI, Text Generation, Deep Learning, GPT, LLaMA, Generative AI
Abstract
Although they may produce fluent text, large language models (LLMs) like GPT-4, LLaMA, and Gemini are prone to hallucinations, which result in coherent-seeming claims that are untrue or unsupported by evidence [1][2]. Hallucinations are dangerous and damage trust (e.g., medical or legal disinformation [3]). This survey thoroughly examines hallucinations in LLMs, providing comprehensive taxonomy of hallucination kinds, underlying causes, methods for identification, and strategies for mitigation. We highlight important results from recent benchmarks (e.g., TruthfulQA, HaluEval, HalluLens) and compare current efforts and classify solutions. An LLM-specific taxonomy (factual vs. fidelity, intrinsic vs. extrinsic, domain and granularity levels) and a critical examination of detection/mitigation techniques (retrieval-augmentation, fine-tuning, prompting) are among our contributions. While open models continue to lag behind GPT-4 on benchmarks, we find that retrieval-augmented and self-consistency approaches help reduce hallucinations [4][5]. We wrap off by discussing future prospects including neurosymbolic grounding and automated fact-checking pipelines, as well as problems like benchmark standardization and multilingual issues.
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IJCRT — journal style
"Hallucination in Large Language Models: A Comprehensive Survey of Types, Detection Methods, Mitigation Strategies, and Future Directions", IJCRT - International Journal of Creative Research Thoughts (www.ijcrt.org), ISSN:2320-2882, Vol.14, Issue 4, page no.j342-j351, April 2026, Available :https://ijcrt.org/papers/IJCRT26A4085.pdf
APA — 7th edition
Patel, 1. J., & Khopkar, 2. M. (2026). Hallucination in Large Language Models: A Comprehensive Survey of Types, Detection Methods, Mitigation Strategies, and Future Directions. International Journal of Creative Research Thoughts, 14(4), j342-j351. https://ijcrt.org/viewpaperforall.php?paper=IJCRT26A4085
MLA — 9th edition
Patel, 1. Jiya, and 2. Manthan Khopkar. "Hallucination in Large Language Models: A Comprehensive Survey of Types, Detection Methods, Mitigation Strategies, and Future Directions." International Journal of Creative Research Thoughts, vol. 14, no. 4, 2026, pp. j342-j351, https://ijcrt.org/viewpaperforall.php?paper=IJCRT26A4085.
Chicago — 17th, bibliography
Patel, 1. Jiya, and 2. Manthan Khopkar. "Hallucination in Large Language Models: A Comprehensive Survey of Types, Detection Methods, Mitigation Strategies, and Future Directions." International Journal of Creative Research Thoughts 14, no. 4 (2026): j342-j351. https://ijcrt.org/viewpaperforall.php?paper=IJCRT26A4085.
Harvard — author–date
Patel, 1.J. and Khopkar, 2.M. (2026) 'Hallucination in Large Language Models: A Comprehensive Survey of Types, Detection Methods, Mitigation Strategies, and Future Directions', International Journal of Creative Research Thoughts, 14(4), pp. j342-j351. Available at: https://ijcrt.org/viewpaperforall.php?paper=IJCRT26A4085
IEEE — numbered reference
1. J. Patel and 2. M. Khopkar, "Hallucination in Large Language Models: A Comprehensive Survey of Types, Detection Methods, Mitigation Strategies, and Future Directions," IJCRT, vol. 14, no. 4, pp. j342-j351, Apr. 2026.
Vancouver — biomedical
Patel 1J, Khopkar 2M. Hallucination in Large Language Models: A Comprehensive Survey of Types, Detection Methods, Mitigation Strategies, and Future Directions. IJCRT. 2026 Apr;14(4):j342-j351.
AMA — 11th edition
Patel 1J, Khopkar 2M. Hallucination in Large Language Models: A Comprehensive Survey of Types, Detection Methods, Mitigation Strategies, and Future Directions. IJCRT. 2026;14(4):j342-j351. Accessed at https://ijcrt.org/viewpaperforall.php?paper=IJCRT26A4085
BibTeX — Zotero, Mendeley, LaTeX
@article{IJCRT26A4085,
author = {1. Jiya Patel and 2. Manthan Khopkar},
title = {Hallucination in Large Language Models: A Comprehensive Survey of Types, Detection Methods, Mitigation Strategies, and Future Directions},
journal = {International Journal of Creative Research Thoughts},
volume = {14},
number = {4},
pages = {j342-j351},
year = {2026},
month = {April},
issn = {2320-2882},
url = {https://ijcrt.org/viewpaperforall.php?paper=IJCRT26A4085}
}
RIS — EndNote, RefWorks
TY - JOUR
AU - Patel, 1. Jiya
AU - Khopkar, 2. Manthan
TI - Hallucination in Large Language Models: A Comprehensive Survey of Types, Detection Methods, Mitigation Strategies, and Future Directions
T2 - International Journal of Creative Research Thoughts
JA - IJCRT
VL - 14
IS - 4
PY - 2026
SN - 2320-2882
UR - https://ijcrt.org/viewpaperforall.php?paper=IJCRT26A4085
SP - j342-j351
ER -
Issue
Volume 14 Issue 4,
April-2026
Pages : j342-j351
Article Information
- Pages
- j342-j351
- Published
- April 2026
- Registration ID
- IJCRT_306590
- Published Paper ID
- IJCRT26A4085
- Research Area
- Science and Technology
- Author Type
- Indian Author
- Location
- Ahmedabad, Gujarat, India
- Peer Review
- Request Peer Review Report
- Licence
- CC BY 4.0 · Open Access
- ISSN & Impact Factor
- 2320-2882 · IF 9.17
- Downloads
- 133
- Permanent Link
- https://ijcrt.org/viewpaperforall.php?paper=IJCRT26A4085
- Published Paper PDF
- https://ijcrt.org/papers/IJCRT26A4085.pdf
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