Can Knowledge Graphs Reduce Hallucinations in LLMs? : A Survey

The contemporary LLMs are prone to producing hallucinations, stemming mainly from the knowledge gaps within the models. To address this critical limitation, researchers employ diverse strategies to augment the LLMs by incorporating external knowledge, aiming to reduce hallucinations and enhance reasoning accuracy. Among these strategies, leveraging knowledge graphs as a source of external information has demonstrated promising results. In this survey, we conduct a comprehensive review of these knowledge-graph-based knowledge augmentation techniques in LLMs, focusing on their efficacy in mitigating hallucinations. We systematically categorize these methods into three overarching groups, offering both methodological comparisons and empirical evaluations of their performance. Lastly, the paper explores the challenges associated with these techniques and outlines potential avenues for future research in this emerging field

Statistical Inference for the Transformed Rayleigh Lomax Distribution with Progressive Type-II Right Censorship

In this paper, we study the transformed Rayleigh Lomax (Trans-RL) distribution which belongs to a certain family of two parameters lifetime distributions given by Wang et al (2010). Confidence intervals and inverse estimators of the Trans-RL parameters are derived in terms of order statistics. A simulation study is conducted to report the coverage probabilities, the average biases and the average relative mean square errors for the maximum likelihood, L-moments and inverse estimators. We compare the performance of these methods under different schemes of progressively Type-II right censoring. Finally, an illustrative example is provided to demonstrate the proposed methods