  The recent popularity of large language models (LLMs) has brought a
significant impact to boundless fields, particularly through their open-ended
ecosystem such as the APIs, open-sourced models, and plugins. However, with
their widespread deployment, there is a general lack of research that
thoroughly discusses and analyzes the potential risks concealed. In that case,
we intend to conduct a preliminary but pioneering study covering the
robustness, consistency, and credibility of LLMs systems. With most of the
related literature in the era of LLM uncharted, we propose an automated
workflow that copes with an upscaled number of queries/responses. Overall, we
conduct over a million queries to the mainstream LLMs including ChatGPT, LLaMA,
and OPT. Core to our workflow consists of a data primitive, followed by an
automated interpreter that evaluates these LLMs under different adversarial
metrical systems. As a result, we draw several, and perhaps unfortunate,
conclusions that are quite uncommon from this trendy community. Briefly, they
are: (i)-the minor but inevitable error occurrence in the user-generated query
input may, by chance, cause the LLM to respond unexpectedly; (ii)-LLMs possess
poor consistency when processing semantically similar query input. In addition,
as a side finding, we find that ChatGPT is still capable to yield the correct
answer even when the input is polluted at an extreme level. While this
phenomenon demonstrates the powerful memorization of the LLMs, it raises
serious concerns about using such data for LLM-involved evaluation in academic
development. To deal with it, we propose a novel index associated with a
dataset that roughly decides the feasibility of using such data for
LLM-involved evaluation. Extensive empirical studies are tagged to support the
aforementioned claims.
