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 Conference Papers 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/. Title A name given to the resource The Troubling Emergence of Hallucination in Large Language Models - An Extensive Definition, Quantification, and Prescriptive Remediations Description An account of the resource The recent advancements in Large Language Models (LLMs) have garnered widespread acclaim for their remarkable emerging capabilities. However, the issue of hallucination has parallelly emerged as a by-product, posing significant concerns. While some recent endeavors have been made to identify and mitigate different types of hallucination, there has been a limited emphasis on the nuanced categorization of hallucination and associated mitigation methods. To address this gap, we offer a fine-grained discourse on profiling hallucination based on its degree, orientation, and category, along with offering strategies for alleviation. As such, we define two overarching orientations of hallucination: (i) factual mirage (FM) and (ii) silver lining (SL). To provide a more comprehensive understanding, both orientations are further sub-categorized into intrinsic and extrinsic, with three degrees of severity - (i) mild, (ii) moderate, and (iii) alarming. We also meticulously categorize hallucination into six types: (i) acronym ambiguity, (ii) numeric nuisance, (iii) generated golem, (iv) virtual voice, (v) geographic erratum, and (vi) time wrap. Furthermore, we curate HallucInation eLiciTation (), a publicly available dataset comprising of 75,000 samples generated using 15 contemporary LLMs along with human annotations for the aforementioned categories. Finally, to establish a method for quantifying and to offer a comparative spectrum that allows us to evaluate and rank LLMs based on their vulnerability to producing hallucinations, we propose Hallucination Vulnerability Index (HVI). Amidst the extensive deliberations on policy-making for regulating AI development, it is of utmost importance to assess and measure which LLM is more vulnerable towards hallucination. We firmly believe that HVI holds significant value as a tool for the wider NLP community, with the potential to serve as a rubric in AI-related policy-making. In conclusion, we propose two solution strategies for mitigating hallucinations. 2023 Association for Computational Linguistics. Creator An entity primarily responsible for making the resource Rawte V.; Chakraborty S.; Pathak A.; Sarkar A.; Tonmoy S.M.T.I.; Chadha A.; Sheth A.; Das A. Source A related resource from which the described resource is derived EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings, pp. 2541-2573. Publisher An entity responsible for making the resource available Association for Computational Linguistics (ACL) Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="" target="_blank" rel="noreferrer noopener"></a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183801765&amp;partnerID=40&amp;md5=61b24e1473804456d88c306889be36d3" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183801765&amp;partnerID=40&amp;md5=61b24e1473804456d88c306889be36d3</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-889176060-8 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Rawte V., AI Institute, University of South Carolina, United States; Chakraborty S., Christ University, India; Pathak A., Christ University, India; Sarkar A., Christ University, India; Tonmoy S.M.T.I., Islamic University of Technology, Bangladesh; Chadha A., Stanford University, United States, Amazon AI, United States; Sheth A., AI Institute, University of South Carolina, United States; Das A., AI Institute, University of South Carolina, United States