The Need for Evaluator Transparency

As LLM-based evaluation (using models to grade other models) becomes the standard for measuring performance, the reliability of these 'evaluators' has become a significant point of failure. MAWILE (Multi-Axis Workbench for Inspecting LLM Evaluators) is designed to move beyond simple accuracy metrics by providing a granular, multi-dimensional inspection of how evaluators make decisions. The core premise is that an evaluator is not a black box; its biases, reasoning patterns, and sensitivity to prompt variations must be auditable to ensure the integrity of the evaluation pipeline.

Multi-Axis Inspection Framework

MAWILE introduces a systematic approach to stress-testing evaluators across several key axes:

• Bias Detection: Identifying systematic preferences for specific response lengths, structures, or stylistic markers that do not correlate with objective quality.
• Sensitivity Analysis: Measuring how minor, semantically neutral changes to the prompt or the target response influence the evaluator's output.
• Reasoning Consistency: Evaluating whether the 'chain-of-thought' provided by the evaluator actually supports the final score, or if the score is generated independently of the justification.
• Calibration: Assessing whether the evaluator's scoring distribution aligns with human-labeled benchmarks or ground-truth datasets.

By decomposing the evaluation process into these axes, MAWILE allows developers to identify exactly where an automated evaluation system breaks down, enabling more targeted prompt engineering and model selection for the evaluation task itself.