Rater Training and Calibration Procedures
Rater Training and Calibration Procedures in LLM Evaluations (evals) refer to the systematic processes used to prepare human evaluators for assessing language model outputs. Training familiarizes raters with evaluation criteria, guidelines, and examples, ensuring consistent understanding. Calibration involves practice rounds and feedback, aligning raters’ judgments and reducing bias or variability. These procedures are essential for achieving reliable, fair, and reproducible evaluation results when measuring the performance of large language models.
Rater Training and Calibration Procedures
Rater Training and Calibration Procedures in LLM Evaluations (evals) refer to the systematic processes used to prepare human evaluators for assessing language model outputs. Training familiarizes raters with evaluation criteria, guidelines, and examples, ensuring consistent understanding. Calibration involves practice rounds and feedback, aligning raters’ judgments and reducing bias or variability. These procedures are essential for achieving reliable, fair, and reproducible evaluation results when measuring the performance of large language models.
💡 Key Takeaways
- Understand the purpose of rater training and calibration in producing consistent scores across raters
- Learn how to create and apply a standardized scoring rubric with clearly defined anchors
- Learn how to run calibration sessions to align raters and improve inter-rater reliability using metrics such as Cohen's kappa or ICC
- Explore strategies for ongoing monitoring, feedback, and refresher trainings to maintain calibration over time
- Identify common sources of bias and error in rating tasks and how to mitigate them during training
❓ Frequently Asked Questions
What is the purpose of rater training in quizzes or assessments?
To teach evaluators how to apply scoring criteria consistently, aiming to reduce bias and improve reliability across raters.
What does calibration mean in rater procedures?
Calibration aligns raters to a shared interpretation of the rubric using anchor examples and practice ratings, ensuring similar judgments.
How is inter-rater reliability typically measured?
Using statistics such as Cohen's kappa (two raters) or Fleiss' kappa / ICC (multiple raters) to quantify agreement beyond chance.
What happens during a calibration session?
Raters review scoring criteria, rate anchor cases, compare results, discuss discrepancies, and adjust understanding until agreement improves.