Mixed Effects Evaluation Model

A Mixed Effects Evaluation Model is a statistical evaluation model that accounts for both fixed effects and random effects to handle hierarchical data structures in evaluation studys.

• AKA: Mixed Model, Hierarchical Linear Model, Multi-Level Model, Random Effects Model with Fixed Effects.
• Context:
  • It can typically model Rater Effects as random factors in human evaluation.
  • It can typically treat Prompt Effects as random samples from prompt population.
  • It can often separate System Performance (fixed) from contextual variation (random).
  • It can often handle Unbalanced Designs with missing observations.
  • It can account for Correlation Structures within nested data.
  • It can provide Variance Component Estimates for effect decomposition.
  • It can support Generalizability Analysis beyond specific samples.
  • It can integrate with REML Estimation for unbiased variance estimates.
  • It can range from being a Two-Level Mixed Model to being a Multi-Level Mixed Model, depending on its hierarchy depth.
  • It can range from being a Linear Mixed Model to being a Generalized Linear Mixed Model, depending on its response distribution.
  • It can range from being a Crossed Mixed Model to being a Nested Mixed Model, depending on its factor structure.
  • It can range from being a Simple Random Intercept Model to being a Random Slope Model, depending on its random effect complexity.
  • ...
• Examples:
  • NLG Mixed Effects Models, such as:
    • Rater-Prompt Mixed Model for evaluation variance.
    • System-Domain Mixed Model for performance analysis.
    • Time-Annotator Mixed Model for longitudinal evaluation.
  • Component Models, such as:
    • Random Intercept Model for baseline variation.
    • Random Slope Model for effect heterogeneity.
    • Crossed Random Effects Model for fully crossed design.
  • Application-Specific Models, such as:
    • Translation Quality Mixed Model for MT evaluation.
    • Dialogue Rating Mixed Model for conversation assessment.
  • ...
• Counter-Examples:
  • Fixed Effects Model, which assumes no random variation.
  • Random Effects Model, which lacks fixed effects.
  • Simple Linear Model, which ignores hierarchical structure.
• See: Statistical Evaluation Model, Hierarchical Model, Linear Mixed Model, Random Effect, Fixed Effect, Stratified Bootstrap Method, Inter-Expert Agreement Metric, Evaluation Protocol, Preference Aggregation Model.

---