Prompt-to-Leaderboard
Authors: name: Evan Frick, name: Connor Chen, name: Joseph Tennyson, name: Tianle Li, name: Wei-Lin Chiang, name: Anastasios N. Angelopoulos, name: Ion Stoica

Large language model (LLM) evaluations typically rely on aggregated metrics
like accuracy or human preference, averaging across users and prompts. This
averaging obscures user- and prompt-specific variations in model performance.
To address this, we propose Prompt-to-Leaderboard (P2L), a method that produces
leaderboards specific to a prompt. The core idea is to train an LLM taking
natural language prompts as input to output a vector of Bradley-Terry
coefficients which are then used to predict the human preference vote. The
resulting prompt-dependent leaderboards allow for unsupervised task-specific
evaluation, optimal routing of queries to models, personalization, and
automated evaluation of model strengths and weaknesses. Data from Chatbot Arena
suggest that P2L better captures the nuanced landscape of language model
performance than the averaged leaderboard. Furthermore, our findings suggest
that P2L's ability to produce prompt-specific evaluations follows a power law
scaling similar to that observed in LLMs themselves. In January 2025, the
router we trained based on this methodology achieved the \#1 spot in the
Chatbot Arena leaderboard. Our code is available at this GitHub link:
https://github.com/lmarena/p2l.