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arXiv · 2502.20170

Re-evaluating Open-ended Evaluation of Large Language Models

Siqi Liu·Ian Gemp·Luke Marris·Georgios Piliouras·Nicolas Heess·Marc Lanctot
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Abstract

Evaluation has traditionally focused on ranking candidates for a specific skill. Modern generalist models, such as Large Language Models (LLMs), decidedly outpace this paradigm. Open-ended evaluation systems, where candidate models are compared on user-submitted prompts, have emerged as a popular solution. Despite their many advantages, we show that the current Elo-based rating systems can be susceptible to and even reinforce biases in data, intentional or accidental, due to their sensitivity to redundancies. To address this issue, we propose evaluation as a 3-player game, and introduce novel game-theoretic solution concepts to ensure robustness to redundancy. We show that our method leads to intuitive ratings and provide insights into the competitive landscape of LLM development.

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BibTeXRIS

Siqi Liu, Ian Gemp, Luke Marris, Georgios Piliouras, Nicolas Heess, Marc Lanctot. 2025-02-27. Re-evaluating Open-ended Evaluation of Large Language Models. https://arxiv.org/abs/2502.20170

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