  Large language models (LLMs) have experienced notable advancements in
generating coherent and contextually relevant responses. However,
hallucinations - incorrect or unfounded claims - are still prevalent, prompting
the creation of automated metrics to detect these in LLM outputs. Our
contributions include: introducing ChainPoll, an innovative hallucination
detection method that excels compared to its counterparts, and unveiling
RealHall, a refined collection of benchmark datasets to assess hallucination
detection metrics from recent studies. While creating RealHall, we assessed
tasks and datasets from previous hallucination detection studies and observed
that many are not suitable for the potent LLMs currently in use. Overcoming
this, we opted for four datasets challenging for modern LLMs and pertinent to
real-world scenarios. Using RealHall, we conducted a comprehensive comparison
of ChainPoll with numerous hallucination metrics from recent studies. Our
findings indicate that ChainPoll outperforms in all RealHall benchmarks,
achieving an overall AUROC of 0.781. This surpasses the next best theoretical
method by 11% and exceeds industry standards by over 23%. Additionally,
ChainPoll is cost-effective and offers greater transparency than other metrics.
We introduce two novel metrics to assess LLM hallucinations: Adherence and
Correctness. Adherence is relevant to Retrieval Augmented Generation workflows,
evaluating an LLM's analytical capabilities within given documents and
contexts. In contrast, Correctness identifies logical and reasoning errors.
