Varco Arena: A Tournament Approach to Reference-Free Benchmarking Large Language Models

• URL: http://arxiv.org/abs/2411.01281v1
• Date: Sat, 02 Nov 2024 15:23:28 GMT
• Title: Varco Arena: A Tournament Approach to Reference-Free Benchmarking Large Language Models
• Authors: Seonil Son, Ju-Min Oh, Heegon Jin, Cheolhun Jang, Jeongbeom Jeong, Kuntae Kim,
• Abstract summary: We propose a more flexible benchmarking approach for Large Language Models (LLMs) Our method, textittextbfVarco Arena, provides reference-free benchmarking of LLMs in tournament style. Our empirical results, supported by simulation experiments, demonstrate that the textittextbfVarco Arena tournament approach aligns better with the current Elo model for benchmarking LLMs.
• Score: 0.29687381456164
• License:
• Abstract: The rapid advancement of Large Language Models (LLMs) necessitates robust evaluation methodologies. Current benchmarking approaches often rely on comparing model outputs against predefined prompts and reference outputs. Relying on predefined reference outputs hinders flexible adaptation of benchmarks to the rapidly evolving capabilities of LLMs. This limitation necessitates periodic efforts to prepare new benchmarks. To keep pace with rapidly evolving LLM capabilities, we propose a more flexible benchmarking approach. Our method, \textit{\textbf{Varco Arena}}, provides reference-free benchmarking of LLMs in tournament style. \textit{\textbf{Varco Arena}} directly compares LLM outputs across a diverse set of prompts, determining model rankings through a single-elimination tournament structure. This direct pairwise comparison offers two key advantages: (1) Direct comparison, unmediated by reference text, more effectively orders competing LLMs, resulting in more reliable rankings, and (2) reference-free approach to benchmarking adds flexibility in updating benchmark prompts by eliminating the need for quality references. Our empirical results, supported by simulation experiments, demonstrate that the \textit{\textbf{Varco Arena}} tournament approach aligns better with the current Elo model for benchmarking LLMs. The alignment is measured in terms of Spearman correlation, showing improvement over current practice of benchmarking that use reference outputs as comparison \textit{anchor}s.

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