  Hallucination continues to be one of the most critical challenges in the
institutional adoption journey of Large Language Models (LLMs). In this
context, an overwhelming number of studies have focused on analyzing the
post-generation phase - refining outputs via feedback, analyzing logit output
values, or deriving clues via the outputs' artifacts. We propose HalluciBot, a
model that predicts the probability of hallucination $\textbf{before
generation}$, for any query imposed to an LLM. In essence, HalluciBot does not
invoke any generation during inference. To derive empirical evidence for
HalluciBot, we employ a Multi-Agent Monte Carlo Simulation using a Query
Perturbator to craft $n$ variations per query at train time. The construction
of our Query Perturbator is motivated by our introduction of a new definition
of hallucination - $\textit{truthful hallucination}$. Our training methodology
generated 2,219,022 estimates for a training corpus of 369,837 queries,
spanning 13 diverse datasets and 3 question-answering scenarios. HalluciBot
predicts both binary and multi-class probabilities of hallucination, enabling a
means to judge the query's quality with regards to its propensity to
hallucinate. Therefore, HalluciBot paves the way to revise or cancel a query
before generation and the ensuing computational waste. Moreover, it provides a
lucid means to measure user accountability for hallucinatory queries.
