Decentralized Arena: Towards Democratic and Scalable Automatic Evaluation of Language Models

TL;DR

Decentralized Arena introduces a fully automated, democratic evaluation framework for language models that leverages collective intelligence, reducing bias and cost while maintaining high correlation with human judgments.

Contribution

We propose a novel decentralized, pairwise evaluation system for LLMs that improves scalability, reduces bias, and automates the creation of evaluation dimensions.

Findings

Achieves up to 97% correlation with human judgments.

Reduces evaluation cost significantly.

Scales efficiently with sub-quadratic complexity.

Abstract

The recent explosion of large language models (LLMs), each with its own general or specialized strengths, makes scalable, reliable benchmarking more urgent than ever. Standard practices nowadays face fundamental trade-offs: closed-ended question-based benchmarks (eg MMLU) struggle with saturation as newer models emerge, while crowd-sourced leaderboards (eg Chatbot Arena) rely on costly and slow human judges. Recently, automated methods (eg LLM-as-a-judge) shed light on the scalability, but risk bias by relying on one or a few "authority" models. To tackle these issues, we propose Decentralized Arena (dearena), a fully automated framework leveraging collective intelligence from all LLMs to evaluate each other. It mitigates single-model judge bias by democratic, pairwise evaluation, and remains efficient at scale through two key components: (1) a coarse-to-fine ranking algorithm for fast incremental insertion of new models with sub-quadratic complexity, and (2) an automatic question selection strategy for the construction of new evaluation dimensions. Across extensive experiments across 66 LLMs, dearena attains up to 97% correlation with human judgements, while significantly reducing the cost. Our code and data will be publicly released on https://github.com/maitrix-org/de-arena.