Elo Uncovered: Robustness and Best Practices in Language Model
  Evaluation
Meriem Boubdir Edward Kim Beyza Ermis Sara Hooker Marzieh Fadaee
In Natural Language Processing (NLP), the Elo rating system, originally
designed for ranking players in dynamic games such as chess, is increasingly
being used to evaluate Large Language Models (LLMs) through "A vs B" paired
comparisons. However, while popular, the system's suitability for assessing
entities with constant skill levels, such as LLMs, remains relatively
unexplored. We study two fundamental axioms that evaluation methods should
adhere to: reliability and transitivity. We conduct extensive evaluation of Elo
behaviour, illustrating that individual Elo computations exhibit volatility and
delving into the impact of varying the Elo rating system's hyperparameters. We
show that these axioms are not always satisfied raising questions about the
reliability of current comparative evaluations of LLMs. If the current use of
Elo scores is intended to substitute the costly head-to-head comparison of
LLMs, it is crucial to ensure the ranking is as robust as possible. Guided by
the axioms, our findings offer concrete guidelines for enhancing the
reliability of LLM evaluation methods, suggesting a need for reassessment of
existing comparative approaches.