The Troubling Emergence of Hallucination in Large Language Models - An Extensive Definition, Quantification, and Prescriptive Remediations (2023)

Abstract: 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 (HILT), 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.

## a holistic view of the hallucination spectrum: its types and scales the issue of hallucination garnered research attention as early as @xcite . however, with the growing size of llms (empirical evidence provided in section 6), there is a corresponding increase in llms' susceptibility to hallucination. consequently, there is a growing interest within the research community to study and understand hallucination to design mitigation techniques. researchers have loosely defined hallucinations and studied various notions of hallucinations in isolation. early exploration of factual vs. nonfactual prompts for checking factuality of lms is addressed in @xcite . a recent survey conducted by @xcite , categorized hallucination into two limited classes: intrinsic and extrinsic. another recent paper @xcite , delved into an intriguing type of hallucination known as name-nationality category hallucination. several other papers @xcite have explored taskspecific categories of hallucination, such as summarization, question-answering, machine translation, etc. preliminary exploration of factual versus non-factual prompts is also addressed in @xcite . however, we purposefully avoid confining our study to a specific task to study the nuances of hallucination. our main contention is that hallucination can occur in any nlg task, necessitating a thorough examination based on the fundamental principles of text generation from a given prompt. the findings from this study can be applied and extended to various nlp tasks. thus, this paper aims to offer a comprehensive categorization of hallucination, as outlined below (see fig. 1 ). ## : hallucination elicitation dataset hilt is a first-of-its-kind publicly available hallucination dataset. to construct this dataset, we have utilized two primary sources of data as prompts: (i) nytimes tweets (nyt) (factually correct -fm) and (ii) the politifact dataset (politifact) (factually incorrect -sl). we selected 15 llms, based on the criteria delineated in section 3.1, and used them to generate a total of 75,000 text passages, with each llm producing 5,000 text prose entries. these entries were categorized as 2,500 each for fm and sl. the text prompts provided to these llms consisted of tweets from nytimes and headlines sourced from the politifact dataset. ## hallucination vulnerability index (hvi) given the growing usage of llms and their likeliness to hallucinate, there exists no uniform evaluation metric to measure these llms' hallucinations. to address this gap, we define hvi, a comparative spectrum that allows us to evaluate and rank llms based on their vulnerability to producing hallucinations. hvi is calculated as in eq. ( 1 ): ## hvi vs. llms size for different llms: an insight from there is a general observation that llms may exhibit a higher tendency towards generating hallucinations or producing outputs that deviate from factual or coherent information. however, it is important to note that the relationship between llm size and hallucination is not necessarily a direct correlation, but rather a consideration based on certain factors such as dence in generated responses. a noteworthy pattern that emerges is that llms without rlhf (reinforcement learning from human feedback) @xcite tend to exhibit a higher tendency for hallucination. although we did not extensively evaluate this phenomenon, we have a keen interest in investigating it further in the near future. while we tried to examine the effect of size on hvi, it looks like there are several other factors contributing to hvi behavior as evident in fig. 6 . thus far, two classes of approaches have been proposed to address the issue of hallucination: (i) preventing llms from hallucinating, which involves implementing strategies during the training and/or generation processes; (ii) mitigating hallucination after generation. (manakul et al., 2023) introduced another taxonomy of classification, categorizing methods into black-box and gray-box. factuality checks during and/or after generation without relying on external resources are known as black-box methods, while those using external resources are referred to as gray-box methods. other hallucination mitigation techniques involve reranking the generated sample responses (dale et al., 2022) and improving beam search @xcite . some recent mitigation techniques @xcite @xcite @xcite @xcite show initial attempts at reducing hallucination. although the complete elimination of hallucination is a complex challenge, this paper explores two plausible directions for mitigation: (i) automatic and (ii) human-in-the-loop. the former is a black-box method where we identify high-entropy words in a given hallucinated text (generated by a high-hvi llm) and replace them with predictions from another llm (lower-hvi). the latter is a gray-box method that involves sentence-level factchecking using textual entailment techniques. this method aims to identify sentences that are deemed susceptible, urging them for human review. 4 ). the outcomes of the detection and replacement strategies discussed earlier are presented in ## conclusion and future avenues the enthusiasm and achievements surrounding llms have led to their widespread adoption, and this trend is only expected to flourish. however, one of the most significant challenges faced by llms today is hallucination. in light of this, benchmark and hallucination vulnerability index (hvi) will continue to serve the wider scientific community and aid policy-makers. benchmark and hvi will be publicly open for further collaborative updates. two proposed mitigation techniques can serve as baselines. limitations: in this paper, we present a unique and extensive benchmark corpus for hallucination called . we propose two main types of hallucination: (i) factual mirage and (ii) silver lining, each further divided into intrinsic and extrinsic subcategories. additionally, we introduce six detailed categories of hallucination along with a measure of its intensity. we believe the following aspects require critical attention in future endeavors. limitation 1: for the sake of simplicity, we have only considered one category per sentence during annotation, although we acknowledge the presence of multi-class and multi-label instances. for instance, in the following example, there are two kinds of hallucination, namely time wrap and numeric nuisance present in the shown sentence. we would like to explore this direction in the immediate future.