CoEval: Ranking Language Models for Custom Tasks Without Labeled Data or Trustworthy Benchmarks
Selecting a pretrained language model, or evaluating a fine-tuned one, for a specific application is a high-value decision, yet the public benchmarks used to make it are poorly suited: a generic benchmark need not reflect a particular sub-domain or sub-task, and its scores are suspect when its items have leaked into pretraining and are recalled rather than solved. We present CoEval, an open framework that supplies a trustworthy, task-specific signal through ensemble self-evaluation: from a task or domain description, a pool of models rotates through all three roles, teacher, student, and judge, to generate a fresh, contamination-free benchmark, answer it, and score one another, with no hu...
| Authors | Alexander Apartsin, Yehudit Aperstein |
|---|---|
| Primary category | cs.CL |
| Published | 2 June 2026 |
| Last updated at source | 4 June 2026 |
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