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

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). 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

Iron pyrite, a potential photovoltaic material, increases plant biomass upon seed pretreatment

Iron pyrite (FeS2) is a promising material with plethora of applications ranging from sulfuric acid production to photo-voltaic devices. Interestingly, the proponents of the theory of hydrothermal origin of life on earth argues that FeS2 may have evolved 4.0 billion years ago, and used as an energy source by the earliest evolving life forms on earth. In the present time, bacteria like Thiobacillus ferrooxidans, which survives in the oxygen deficient environments, derives energy solely from FeS2 to maintain its critical biomass. The key question, we addressed in this paper is 'whether higher plants have the ability to derive energy solely from FeS2, just like the way Thiobacillus species does.' To answer this question, we developed a novel, inexpensive, low temperature scheme (< 100 °C) for FeS2 synthesis. We characterize FeS2 using X-ray diffraction (XRD), scanning electron microscopy (SEM) and high-resolution transmission electron microscopy (HRTEM) techniques. Further, we pretreated the chick-pea (Legume) seeds for 12 hours in sterile, double distilled aqueous medium of dispersed FeS2 (80 μg/ml). Following this, we allowed these seeds to grow in sterile, double distilled water for 7 days. At this stage, we observed that FeS2 pre-treated seeds result in significantly healthier plants, with increased dryweight and enhanced sulfur content as compared to the control plants. In summary, a brief FeS2 pre-treatment of the seeds resulted in increased plant biomass. This study has drawn an evolutionary consilience between two diverse life forms in terms of their ability to use a common pre-biotic energy molecule for biomass production. Our results suggest that FeS2 apart from its opto-electronic potential, could also be used as a pro-fertilizer for sustainable agriculture practices