title: Automatic Calibration and Error Correction for Large Language Models via
    Pareto Optimal Self-Supervision
summary: 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.
paper_authors._id: 649e2b1f2ccae3ea1f2c8e13
paper_authors.name: Theodore Zhao
paper_authors._id: 649e2b1f2ccae3ea1f2c8e14
paper_authors.name: Mu Wei
paper_authors._id: 649e2b1f2ccae3ea1f2c8e15
paper_authors.name: J. Samuel Preston
paper_authors._id: 649e2b1f2ccae3ea1f2c8e16
paper_authors.name: Hoifung Poon
paper_title: Automatic Calibration and Error Correction for Large Language Models via
    Pareto Optimal Self-Supervision
paper_summary: 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.
paper_ai_summary: A Pareto optimal self-supervision framework uses available programmatic supervision to calibrate LLM responses, producing risk scores that correlate with error rates and improve accuracy through dynamic prompting.
paper_ai_keywords: large language models LLMs programmatic supervision self-supervision framework harmonizer model risk score relation extraction dynamic prompting GPT-3 GPT-4 weak supervision supervised results evaluation datasets