Confidence and Stability of Global and Pairwise Scores in NLP Evaluation
This work helps NLP researchers and practitioners choose appropriate evaluation strategies by empirically comparing two common approaches.
The paper investigates the reliability of global scores versus pairwise comparisons for evaluating NLP models, finding that global scores provide more reliable overall rankings but can underestimate strong models with rare errors, while pairwise comparisons better identify strong contenders among lower-scoring models but require more comparisons to converge.
With the advent of highly capable instruction-tuned neural language models, benchmarking in natural language processing (NLP) is increasingly shifting towards pairwise comparison leaderboards, such as LMSYS Arena, from traditional global pointwise scores (e.g., GLUE, BIG-bench, SWE-bench). This paper empirically investigates the strengths and weaknesses of both global scores and pairwise comparisons to aid decision-making in selecting appropriate model evaluation strategies. Through computational experiments on synthetic and real-world datasets using standard global metrics and the popular Bradley-Terry model for pairwise comparisons, we found that while global scores provide more reliable overall rankings, they can underestimate strong models with rare, significant errors or low confidence. Conversely, pairwise comparisons are particularly effective for identifying strong contenders among models with lower global scores, especially where quality metrics are hard to define (e.g., text generation), though they require more comparisons to converge if ties are frequent. Our code and data are available at https://github.com/HSPyroblast/srw-ranking under a permissive license.