BetterBench: Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practices (2411.12990v1)

Abstract: AI models are increasingly prevalent in high-stakes environments, necessitating thorough assessment of their capabilities and risks. Benchmarks are popular for measuring these attributes and for comparing model performance, tracking progress, and identifying weaknesses in foundation and non-foundation models. They can inform model selection for downstream tasks and influence policy initiatives. However, not all benchmarks are the same: their quality depends on their design and usability. In this paper, we develop an assessment framework considering 46 best practices across an AI benchmark's lifecycle and evaluate 24 AI benchmarks against it. We find that there exist large quality differences and that commonly used benchmarks suffer from significant issues. We further find that most benchmarks do not report statistical significance of their results nor allow for their results to be easily replicated. To support benchmark developers in aligning with best practices, we provide a checklist for minimum quality assurance based on our assessment. We also develop a living repository of benchmark assessments to support benchmark comparability, accessible at betterbench.stanford.edu.

• The paper introduces the BetterBench framework, utilizing 46 criteria to assess the quality of AI benchmarks throughout their lifecycle.
• Evaluation of 24 benchmarks revealed significant gaps, particularly in implementation and statistical rigor, with most lacking replication scripts or statistical significance reporting.
• BetterBench offers a checklist for benchmark developers and a living repository to promote best practices and enhance the reliability and comparability of AI evaluations.

An Expert Analysis of "BetterBench: Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practices"

This paper introduces "BetterBench," a structured assessment framework designed to enhance the quality of AI benchmarks. As the deployment of AI models in high-stakes environments grows, thoroughly assessing these models’ capabilities and potential risks is increasingly vital. Benchmarks serve as critical tools for evaluating these attributes, comparing model performance, tracking progress, and identifying weaknesses in both foundation and non-foundation models. However, as this paper identifies, significant quality differences exist across widely used benchmarks, underscoring the need for a more rigorous, standardized approach to benchmark assessment.

Core Contributions and Findings

The authors present a novel framework comprising 46 criteria derived from expert interviews and domain literature to evaluate the quality of AI benchmarks throughout their lifecycle. This framework was applied to 24 AI benchmarks, and the findings indicate substantial gaps in quality, particularly in the areas of implementation and statistical rigor. The research highlights that most benchmarks fail to report statistical significance and lack comprehensive documentation, which undermines their utility and transparency. To address these issues, the authors developed a checklist to guide benchmark developers in aligning with best practices and created a living repository to enhance benchmark comparability.

Technical Analysis and Numerical Insights

The BetterBench framework evaluates benchmarks based on criteria spanning their design, implementation, documentation, and maintenance phases. The assessment uses a discrete scoring system to quantify compliance with best practices. Results from the evaluation of 24 benchmarks revealed that the implementation stage received the lowest scores on average, indicating widespread issues in providing accessible evaluation code and supporting documentation. For instance, 17 out of the 24 benchmarks did not include scripts to replicate initial published results, and 14 benchmarks did not report the statistical significance of their findings.

Implications for AI Development and Policy

The implications of this work are twofold: practical and theoretical. Practically, the systematic assessment and accompanying checklist serve as tools to improve the quality and reliability of AI benchmarks, thereby facilitating more informed decisions in model selection and regulatory assessments. Theoretically, the identification of common shortcomings across benchmarks prompts a reevaluation of current AI evaluation methodologies and highlights areas for future research, such as the development of more dynamic and robust benchmarking practices that can account for rapid advancements in AI capabilities.

The paper also touches upon regulatory and policy implications. Given the increasing reliance on benchmarks in policy initiatives, ensuring the quality and credibility of these benchmarks is critical for effective governance and oversight of AI technologies. The BetterBench framework could inform regulations that require transparency and rigorous evaluation standards for AI models.

Future Developments and Challenges

Looking ahead, the continuous update and expansion of the BetterBench repository will be crucial as the landscape of AI benchmarks evolves. Furthermore, addressing open challenges in AI benchmarking, such as contamination and quick saturation, will require collaborative efforts among stakeholders, including model developers, policymakers, and researchers. As AI systems become more integrated into societal functions, the need for high-quality benchmarks that can validly and reliably assess model performance will only increase.

In conclusion, BetterBench represents a significant step towards standardizing and improving AI benchmark quality, offering a structured approach and practical tools for benchmark developers. The framework’s adoption could lead to more reliable and transparent AI evaluations, ultimately supporting the responsible advancement of AI technologies.