arXiv:2508.11847v4 Announce Type: replace-cross Abstract: We propose a method for evaluating the robustness of widely used LLM ranking systems -- variants of a Bradley--Terry model -- to dropping a worst-case very small fraction of preference data. Our approach is computationally fast and easy to adopt. When we apply our method to matchups from popular LLM ranking platforms, including Chatbot Arena and derivatives, we find that the rankings of top-performing models can be remarkably sensitive to the removal of a small fraction of preferences; for instance, dropping just 0.003% of human prefere
The proliferation of LLMs and the increasing reliance on leaderboards for performance evaluation makes the robustness of these ranking systems a critical and timely concern.
This research reveals the inherent fragility and potential for manipulation in current LLM ranking methodologies, which directly influences adoption, investment, and public perception of AI capabilities.
The understanding that top LLM rankings can be significantly swayed by minor data perturbations necessitates a re-evaluation of how LLMs are benchmarked and trusted.
- · White-hat security researchers
- · Independent AI evaluators
- · LLMs with demonstrable robustness
- · LLM ranking platforms (if they don't adapt)
- · LLMs with superficial performance
- · Relying solely on leaderboard positions
Increased scrutiny and skepticism towards existing LLM leaderboards.
Development of more robust and auditable LLM evaluation methodologies.
A shift in investor and consumer confidence towards LLMs evaluated against more rigorous, perturbation-resistant benchmarks.
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Read at arXiv cs.LG