Re-evaluating Open-ended Evaluation of Large Language Models

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

Keywords: Evaluation, Game Theory, Large Language Model, Equilibrium, Open-Ended

TL;DR: We study open-ended LLM evaluation and propose a scalable equilibrium rating framework that provides robust and interpretable ratings of models and prompts.

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.

Primary Area: other topics in machine learning (i.e., none of the above)

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Submission Number: 7064