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Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference (2403.04132v1)

Published 7 Mar 2024 in cs.AI and cs.CL

Abstract: LLMs have unlocked new capabilities and applications; however, evaluating the alignment with human preferences still poses significant challenges. To address this issue, we introduce Chatbot Arena, an open platform for evaluating LLMs based on human preferences. Our methodology employs a pairwise comparison approach and leverages input from a diverse user base through crowdsourcing. The platform has been operational for several months, amassing over 240K votes. This paper describes the platform, analyzes the data we have collected so far, and explains the tried-and-true statistical methods we are using for efficient and accurate evaluation and ranking of models. We confirm that the crowdsourced questions are sufficiently diverse and discriminating and that the crowdsourced human votes are in good agreement with those of expert raters. These analyses collectively establish a robust foundation for the credibility of Chatbot Arena. Because of its unique value and openness, Chatbot Arena has emerged as one of the most referenced LLM leaderboards, widely cited by leading LLM developers and companies. Our demo is publicly available at \url{https://chat.lmsys.org}.

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Citations (263)
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Summary

  • The paper introduces a novel crowdsourced pairwise comparison method that collected over 240K votes to evaluate LLMs based on human preferences.
  • It employs advanced statistical tools, including Bradley-Terry models and adaptive sampling algorithms, to enhance ranking accuracy and detect anomalous behavior.
  • The platform’s diverse prompts and high correlation with expert ratings underscore its effectiveness in benchmarking LLM performance.

An Open Platform for Evaluating LLMs by Human Preference

Introduction

The rapid development of LLMs has posed new challenges in evaluating their performance, particularly concerning alignment with human preferences. Traditional benchmarks, often static and lacking in diversity, fail to fully capture the nuances of these advanced models. Addressing this gap, the introduction of \system provides a groundbreaking platform facilitating the evaluation of LLMs based on human preferences. It leverages a pairwise comparison methodology and crowdsourcing to compile a substantial volume of over 240K votes from a broad user base. This paper details the platform's design, the statistical mechanisms underpinning its model evaluations, and discusses the implications of this work for the future of LLM evaluation.

Crowdsourced Data Collection

At the core of \system is its innovative approach to data collection, relying on a crowdsourced, pairwise comparison method wherein users interact with anonymous models and cast their preferences. To date, this methodology has amassed over 240K votes across more than 50 models, reflecting a diverse set of languages. The platform’s design emphasizes diversity in user-generated prompts, ensuring a comprehensive evaluation that mirrors real-world use cases.

Statistical Foundations for Model Evaluation

A sophisticated suite of statistical tools underlies \system’s evaluation process. Utilizing models from Bradley-Terry to E-values, the platform can estimate rankings with improved efficiency and accuracy. This methodology not only ensures a robust model comparison but also allows for the strategic sampling of model pairs, enhancing the convergence of rankings while maintaining statistical integrity. This statistical approach has allowed for a highly effective evaluation mechanism within \system.

Data Analysis and Insights

A thorough analysis of the collected data confirms the platform's capacity to generate diverse and challenging prompts that effectively discriminate between models. Additionally, a comparison against expert ratings reveals a high degree of agreement, validating the reliability of crowdsourced votes. The platform also enables the construction of challenging benchmarks that can accentuate the differences between leading models, further showcasing the effectiveness of \system's approach.

Efficient Ranking Estimation and Anomalous User Detection

\system introduces an adaptive sampling algorithm that significantly enhances the platform's efficiency in estimating model rankings. Parallelly, the paper outlines a novel method for identifying anomalous user behaviors, ensuring the integrity of the data collected. These technological advancements denote significant strides forward in the methodology of LLM evaluation.

Implications and Forward Look

The establishment of \system as a leading platform for LLM evaluation marks a pivotal advance in the field. It not only addresses the critical need for a dynamic and human-centric evaluation mechanism but also sets the stage for future developments in AI and machine learning evaluation. As \system evolves, it is set to incorporate more comprehensive features, including topic leaderboards and support for multimodal and agent-based LLMs, promising an even richer evaluation landscape.

Conclusion

In conclusion, \system represents a significant leap forward in the methodology of evaluating LLMs, fostering a more dynamic, accurate, and human-aligned approach. By harnessing crowdsourced human preferences and employing rigorous statistical methods, this platform ensures a comprehensive and nuanced assessment of LLMs, paving the way for future innovations in AI evaluation.

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