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On Evaluating LLM Alignment by Evaluating LLMs as Judges

Kari Jaaskelainen

— 1 min read
On Evaluating LLM Alignment by Evaluating LLMs as Judges

How do we know if an AI model is truly aligned with human preferences—helpful, honest, safe, and instruction-following? This paper explores a surprisingly effective shortcut: judge the judges.

Instead of grading a model’s open-ended answers (which needs lots of human effort or very strong AI judges), the authors test models on how well they evaluate others. They find strong generation–evaluation consistency: models that write good answers also tend to be good at judging answers—when measured against a strong preference oracle.

  • Introduce AlignEval, a benchmark that ranks models by their judging ability, skipping direct output evaluation.
  • AlignEval matches or beats popular automatic benchmarks like AlpacaEval and Arena-Hard at capturing human preferences.
  • Provides a scalable, low-cost path to monitor alignment across models.

Why it matters: If models that judge well are also aligned, we can track alignment reliably without scoring every response.

Paper by Yixin Liu, Pengfei Liu, Arman Cohan. More: https://arxiv.org/abs/2511.20604v1

Paper: https://arxiv.org/abs/2511.20604v1

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