Ranking Unraveled: Recipes for LLM Rankings in Head-to-Head AI Combat

2024-11-19

arXiv.org

AI Summary

This study addresses the reliability and validity of human preference ranking among large language models (LLMs). To tackle challenges—including noisy pairwise annotations, data sparsity, and cold-start issues—we formally propose six fundamental principles for LLM ranking. We systematically evaluate Elo and its variants across diverse evaluation scenarios, leveraging human-annotated pairwise data, statistical robustness analysis, and large-scale ablation studies. Based on these insights, we develop a principled algorithm selection guide tailored to resource constraints and evaluation objectives. Experimental results demonstrate that our framework significantly improves the stability, accuracy, and reproducibility of relative LLM capability assessment. It provides both theoretical foundations and practical tools for scientifically selecting LLMs and constructing rigorous, trustworthy benchmarks.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Natural Language Processing: (Large) Language Models
• Humans and AI: Learning Human Values and Preferences

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Deciding which large language model (LLM) to use is a complex challenge. Pairwise ranking has emerged as a new method for evaluating human preferences for LLMs. This approach entails humans evaluating pairs of model outputs based on a predefined criterion. By collecting these comparisons, a ranking can be constructed using methods such as Elo. However, applying these algorithms as constructed in the context of LLM evaluation introduces several challenges. In this paper, we explore the effectiveness of ranking systems for head-to-head comparisons of LLMs. We formally define a set of fundamental principles for effective ranking and conduct a series of extensive evaluations on the robustness of several ranking algorithms in the context of LLMs. Our analysis uncovers key insights into the factors that affect ranking accuracy and efficiency, offering guidelines for selecting the most appropriate methods based on specific evaluation contexts and resource constraints.

Problem

• Evaluating LLMs using pairwise ranking
• Defining principles for effective LLM ranking
• Assessing ranking algorithm robustness for LLMs

Innovation

• Pairwise ranking evaluation
• Elo algorithm application
• LLM robustness analysis