As large language models (LLMs) become increasingly sophisticated, ensuring fair and unbiased evaluation has become a critical challenge. Existing evaluation protocols often suffer from benchmark contamination, where models are trained on datasets that include portions of the test benchmarks, leading to artificially inflated results. A recent approach known as Agents-as-an-Evaluator attempts to address this issue by generating new test questions...
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