WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in
  the Wild
Authors: name: Bill Yuchen Lin, name: Yuntian Deng, name: Khyathi Chandu, name: Faeze Brahman, name: Abhilasha Ravichander, name: Valentina Pyatkin, name: Nouha Dziri, name: Ronan Le Bras, name: Yejin Choi

We introduce WildBench, an automated evaluation framework designed to
benchmark large language models (LLMs) using challenging, real-world user
queries. WildBench consists of 1,024 tasks carefully selected from over one
million human-chatbot conversation logs. For automated evaluation with
WildBench, we have developed two metrics, WB-Reward and WB-Score, which are
computable using advanced LLMs such as GPT-4-turbo. WildBench evaluation uses
task-specific checklists to evaluate model outputs systematically and provides
structured explanations that justify the scores and comparisons, resulting in
more reliable and interpretable automatic judgments. WB-Reward employs
fine-grained pairwise comparisons between model responses, generating five
potential outcomes: much better, slightly better, slightly worse, much worse,
or a tie. Unlike previous evaluations that employed a single baseline model, we
selected three baseline models at varying performance levels to ensure a
comprehensive pairwise evaluation. Additionally, we propose a simple method to
mitigate length bias, by converting outcomes of ``slightly better/worse'' to
``tie'' if the winner response exceeds the loser one by more than K
characters. WB-Score evaluates the quality of model outputs individually,
making it a fast and cost-efficient evaluation metric. WildBench results
demonstrate a strong correlation with the human-voted Elo ratings from Chatbot
Arena on hard tasks. Specifically, WB-Reward achieves a Pearson correlation of
0.98 with top-ranking models. Additionally, WB-Score reaches 0.95, surpassing
both ArenaHard's 0.91 and AlpacaEval2.0's 0.89 for length-controlled win rates,
as well as the 0.87 for regular win rates.