  Despite their impressive performance, large language models (LLMs) such as
ChatGPT are known to pose important risks. One such set of risks arises from
misplaced confidence, whether over-confidence or under-confidence, that the
models have in their inference. While the former is well studied, the latter is
not, leading to an asymmetry in understanding the comprehensive risk of the
model based on misplaced confidence. In this paper, we address this asymmetry
by defining two types of risk (decision and composite risk), and proposing an
experimental framework consisting of a two-level inference architecture and
appropriate metrics for measuring such risks in both discriminative and
generative LLMs. The first level relies on a decision rule that determines
whether the underlying language model should abstain from inference. The second
level (which applies if the model does not abstain) is the model's inference.
Detailed experiments on four natural language commonsense reasoning datasets
using both an open-source ensemble-based RoBERTa model and ChatGPT, demonstrate
the practical utility of the evaluation framework. For example, our results
show that our framework can get an LLM to confidently respond to an extra 20.1%
of low-risk inference tasks that other methods might misclassify as high-risk,
and skip 19.8% of high-risk tasks, which would have been answered incorrectly.
