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Interactive 3D/LLM-as-Judge Evaluation
Rubric
Judge Model
N Judges
Judges1

LLM-as-Judge Evaluation - Interactive Visualization

LLM-as-judge uses a strong model to evaluate generated outputs against explicit rubrics - scaling human evaluation without hiring annotators. G-Eval scores on helpfulness, factuality, safety, and coherence. A key challenge is position bias: the judge often prefers whichever response is listed first. This demo reveals this bias with a swap test.

  • Rubric scoring: helpfulness, factuality, safety, and coherence scored 1-5 per response
  • Pairwise comparison: judge rates Response A vs Response B and picks a winner
  • Position bias test: swap A and B order and see if the judge flips its preference
  • Bias magnitude: measured as the percentage of cases where order changes the verdict

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.