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Common Mistakes in Evaluation

One frequent error is over-reliance on a single metric, which fails to capture the multidimensional nature of language tasks. Using outdated benchmarks can misrepresent modern model abilities or ignore emerging challenges. Data leakage—where test data overlaps with training data—can artificially inflate scores and mislead evaluations.

Best Practices

Combining human and automatic evaluation leverages the speed of algorithms and the insightfulness of human judgment. Regularly updating benchmarks ensures evaluations remain relevant amid rapid LLM advancements. Robustness testing against adversarial inputs and real-world scenarios helps assess how models perform outside controlled environments.