Automatic Calibration and Error Correction for Large Language Models via
  Pareto Optimal Self-Supervision
Authors: name: Theodore Zhao, name: Mu Wei, name: J. Samuel Preston, name: Hoifung Poon

Large language models (LLMs) have demonstrated remarkable capabilities out of
box for a wide range of applications, yet accuracy still remains a major growth
area, especially in mission-critical domains such as biomedicine. An effective
method to calibrate the confidence level on LLM responses is essential to
automatically detect errors and facilitate human-in-the-loop verification. An
important source of calibration signals stems from expert-stipulated
programmatic supervision, which is often available at low cost but has its own
limitations such as noise and coverage. In this paper, we introduce a Pareto
optimal self-supervision framework that can leverage available programmatic
supervision to systematically calibrate LLM responses by producing a risk score
for every response, without any additional manual efforts. This is accomplished
by learning a harmonizer model to align LLM output with other available
supervision sources, which would assign higher risk scores to more uncertain
LLM responses and facilitate error correction. Experiments on standard relation
extraction tasks in biomedical and general domains demonstrate the promise of
this approach, with our proposed risk scores highly correlated with the real
error rate of LLMs. For the most uncertain test instances, dynamic prompting
based on our proposed risk scores results in significant accuracy improvement
for off-the-shelf LLMs, boosting GPT-3 results past state-of-the-art (SOTA)
weak supervision and GPT-4 results past SOTA supervised results on challenging
evaluation datasets.