Building better AI benchmarks: How many raters are enough?
Human disagreement makes AI benchmarks difficult to reproduce, yet evaluations often use only one to five raters per item and reduce their responses to a majority vote. The study introduces an `(N, K)` framework—balancing the number of items (`N`) against raters per item (`K`)—to determine how annotation budgets should be allocated. It concludes that the best balance depends on the evaluation goal: broad sampling for majority accuracy, but deeper rating for capturing nuanced human opinions. ## The Breadth-versus-Depth Trade-off - The “forest” strategy rates many items with few raters per item. - The “tree” strategy rates fewer items with many raters per item. - Historically, AI benchmarks have favored the forest approach, typically using one to five raters per example. - This approach can miss both the overall distribution of opinions and meaningful disagreement among raters. ## Simulating Annotation Budgets - The researchers built a simulator using real-world subjective datasets, including toxicity, hate speech, safety, offensiveness, and job-related tweet classification. - They varied: - **Scale (`N`)**: 100 to 50,000 total items. - **Crowd (`K`)**: 1 to 500 raters per item. - Thousands of configurations were tested for statistical reliability, including whether model comparisons reached significance at `p < 0.05`. - The simulator also examined messy conditions such as highly imbalanced categories and tasks with multiple labels. - The simulator has been released as open source. ## Why Three to Five Raters Are Often Insufficient - Low-rater evaluations may fail to represent natural human disagreement. - They provide too little depth to reveal nuanced opinions and too little breadth to establish a reliable overall picture. - In many settings, more than 10 raters per item are needed to produce results that reflect the variation in human judgments. - More ratings per item can make model comparisons more statistically reliable. ## The Evaluation Metric Determines the Optimal Strategy - **Majority-vote accuracy** - If the goal is to determine whether a model agrees with the majority of people, rating more items is generally more effective. - This favors the forest strategy. - **Opinion range and nuance** - If the evaluation must distinguish between responses such as “yes,” “maybe,” and “no,” more raters per item are essential. - This favors the tree strategy because only repeated ratings reveal the full distribution of human opinions. - There is no universally optimal number of items or raters; the correct allocation depends on what the benchmark is intended to measure. ## Reproducibility Without Unlimited Budgets - An appropriately chosen item-to-rater ratio can produce highly reproducible results with roughly 1,000 total annotations in some settings. - Spending more money does not guarantee reliability if the budget is distributed poorly. - The study’s framework is intended to help benchmark designers choose the allocation that best fits their metric and data characteristics. ## Moving Beyond a Single Ground Truth - Many AI evaluations assume that every example has one objectively correct label. - This assumption becomes increasingly problematic for subjective tasks involving toxicity, harmful intent, ethics, safety, or social interaction. - Preserving disagreement instead of collapsing it into a plurality label can make benchmarks more representative of real human judgment. - The authors argue that understanding disagreement is as important as measuring consensus. Benchmark designers should first decide whether they need majority accuracy or a detailed picture of human opinion, then allocate ratings accordingly. In subjective evaluations, using substantially more than five raters per item may be necessary for reliable and reproducible conclusions.
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