  As Large Language Models (LLMs) become more pervasive across various users
and scenarios, identifying potential issues when using these models becomes
essential. Examples of such issues include: bias, inconsistencies, and
hallucination. Although auditing the LLM for these problems is often warranted,
such a process is neither easy nor accessible for most. An effective method is
to probe the LLM using different versions of the same question. This could
expose inconsistencies in its knowledge or operation, indicating potential for
bias or hallucination. However, to operationalize this auditing method at
scale, we need an approach to create those probes reliably and automatically.
In this paper we propose the LLMAuditor framework which is an automatic, and
scalable solution, where one uses a different LLM along with human-in-the-loop
(HIL). This approach offers verifiability and transparency, while avoiding
circular reliance on the same LLM, and increasing scientific rigor and
generalizability. Specifically, LLMAuditor includes two phases of verification
using humans: standardized evaluation criteria to verify responses, and a
structured prompt template to generate desired probes. A case study using
questions from the TruthfulQA dataset demonstrates that we can generate a
reliable set of probes from one LLM that can be used to audit inconsistencies
in a different LLM. This process is enhanced by our structured prompt template
with HIL, which not only boosts the reliability of our approach in auditing but
also yields the delivery of less hallucinated results. The novelty of our
research stems from the development of a comprehensive, general-purpose
framework that includes a HIL verified prompt template for auditing responses
generated by LLMs.
