  Contemporary AI applications leverage large language models (LLMs) to harness
their knowledge and reasoning abilities for natural language processing tasks.
This approach shares similarities with the concept of oracle Turing machines
(OTMs). To capture the broader potential of these computations, including those
not yet realized, we propose an extension to OTMs: the LLM-oracle machine
(LLM-OM), by employing a cluster of LLMs as the oracle. Each LLM acts as a
black box, capable of answering queries within its expertise, albeit with a
delay. We introduce four variants of the LLM-OM: basic, augmented,
fault-avoidance, and $\epsilon$-fault. The first two are commonly observed in
existing AI applications. The latter two are specifically designed to address
the challenges of LLM hallucinations, biases, and inconsistencies, aiming to
ensure reliable outcomes.
