A Formalism and Approach for Improving Robustness of Large Language
  Models Using Risk-Adjusted Confidence Scores
Ke Shen Mayank Kejriwal
Large Language Models (LLMs), such as ChatGPT, have achieved impressive
milestones in natural language processing (NLP). Despite their impressive
performance, the models are known to pose important risks. As these models are
deployed in real-world applications, a systematic understanding of different
risks posed by these models on tasks such as natural language inference (NLI),
is much needed. In this paper, we define and formalize two distinct types of
risk: decision risk and composite risk. We also propose a risk-centric
evaluation framework, and four novel metrics, for assessing LLMs on these risks
in both in-domain and out-of-domain settings. Finally, we propose a
risk-adjusted calibration method called DwD for helping LLMs minimize these
risks in an overall NLI architecture. Detailed experiments, using four NLI
benchmarks, three baselines and two LLMs, including ChatGPT, show both the
practical utility of the evaluation framework, and the efficacy of DwD in
reducing decision and composite risk. For instance, when using DwD, an
underlying LLM is able to address an extra 20.1% of low-risk inference tasks
(but which the LLM erroneously deems high-risk without risk adjustment) and
skip a further 19.8% of high-risk tasks, which would have been answered
incorrectly.