UQLM: The Secret Weapon for Making Large Language Models More Trustworthy

What is UQLM?

UQLM (Uncertainty Quantification for Language Models) is an open-source Python library developed by CVS Health. It is designed to detect hallucinations in large language model (LLM) outputs using uncertainty quantification techniques. UQLM provides developers and researchers with a suite of advanced scorers to assess the reliability of LLM outputs, helping to identify and reduce the spread of misinformation.

Key Features

• Multiple Scorer Types:
  Includes black-box scorers, white-box scorers, LLM-as-a-judge scorers, and ensemble scorers to meet different evaluation needs.

• Response-Level Confidence Scoring:
  Each scorer returns a confidence score between 0 and 1, indicating the likelihood that the model’s output is trustworthy.

• Compatibility with Various LLMs:
  Designed to work with any large language model, offering great flexibility.

• Out-of-the-Box Usability:
  Easy-to-use interfaces allow for quick integration into existing workflows.

Technical Principles

• Black-box Scorers:
  Assess the consistency of multiple generated responses to estimate uncertainty—without needing internal access to the model.

• White-box Scorers:
  Use token-level probability data from the model to quickly evaluate uncertainty, suitable for models that expose token probabilities.

• LLM-as-a-Judge Scorers:
  Employ one or more LLMs to review and critique the original model’s output, offering highly customizable evaluation methods.

• Ensemble Scorers:
  Combine results from multiple scorers to deliver robust confidence estimates, ideal for high-stakes applications.

Project Links

• GitHub Repository: https://github.com/cvs-health/uqlm

• Official Documentation: https://cvs-health.github.io/uqlm/latest/index.html

Use Cases

• Healthcare:
  Ensure the accuracy of AI-generated medical advice and reduce the risk of misinformation.

• Legal Document Drafting:
  Evaluate the reliability of legal text outputs and assist legal professionals in review processes.

• Educational Content Creation:
  Detect potential inaccuracies in educational materials to enhance the quality of learning resources.

• Customer Service Automation:
  Validate the correctness of automated replies to improve customer satisfaction.