Wildbench: Benchmarking Llms With Challenging Tasks From Real Users In The Wild
Lin Bill Yuchen, Deng Yuntian, Chandu Khyathi, Brahman Faeze, Ravichander Abhilasha, Pyatkin Valentina, Dziri Nouha, Bras Ronan Le, Choi Yejin. Arxiv 2024
[Paper]
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.
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