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The Troubling Emergence of Hallucination in Large Language Models - An Extensive Definition, Quantification, and Prescriptive Remediations
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Vipula Rawte, Swagata Chakraborty, Agnibh Pathak, Anubhav Sarkar, S.M Towhidul Islam Tonmoy, Aman Chadha, Amit Sheth, Amitava Das. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.
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Extensive research exists on hallucinations in large language models, covering their causes and sources, and methods to evaluate how prone models are to producing them.
“There is extensive work on hallucinations in LLMs [48,54], including, the causes and sources of hallucinations [77], and measures for evaluating LLMs based on their vulnerability to producing hallucinations [94].”
From Grounding and Evaluation for Large Language Models: Practical Challenges and Lessons Learned (Survey) (2024) · cites this paper as [94]
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Metadata
- Authors
- Rawte, V., Chakraborty, S., Pathak, A., Sarkar, A., Tonmoy, S. M. T. I., Chadha, A., Sheth, A., & Das, A.
- Affiliations
- University of South Carolina (US)
- Christ University (IN)
- Islamic University of Technology (BD)
- Amazon (United States) (US)
- Corresponding author
- Rawte, Vipula
- Venue
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Published
- 2023
- Pages
- 2541-2573
- OpenAlex
- W4389524379
- Cited by
- 119
- References
- 29
- Citations by year
- 2026: 35 · 2025: 49 · 2024: 33 · 2023: 2
- FWCI
- 21.08
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- hallucinations, large language models, factual consistency
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