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Risk Management for Mitigating Benchmark Failure Modes: BenchRisk

Sean McGregor, Victor Lu, Vassil Tashev, Armstrong Foundjem, Aishwarya Ramasethu, Sadegh AlMahdi Kazemi Zarkouei, Chris Knotz, Kongtao Chen, Alicia Parrish, Anka Reuel, Heather Frase

TL;DR

This work addresses the problem that LLM benchmarks may mislead real-world decisions due to failure modes affecting bias, variance, coverage, and user interpretation. It introduces BenchRisk, a risk-management framework inspired by NIST practices that quantifies benchmark reliability via 57 failure modes and 196 mitigations across five dimensions. The approach defines a risk metric $R = s \cdot l$, applies mitigations that reduce $s$ (severity) and $l$ (likelihood), and aggregates results into an open, community-driven score at benchrisk.ai. Findings reveal pervasive risk across most benchmarks, with longevity often weak, and demonstrate how an open, iterative evaluation workflow can enable benchmark authors and users to compare reliability and prioritize improvements.

Abstract

Large language model (LLM) benchmarks inform LLM use decisions (e.g., "is this LLM safe to deploy for my use case and context?"). However, benchmarks may be rendered unreliable by various failure modes that impact benchmark bias, variance, coverage, or people's capacity to understand benchmark evidence. Using the National Institute of Standards and Technology's risk management process as a foundation, this research iteratively analyzed 26 popular benchmarks, identifying 57 potential failure modes and 196 corresponding mitigation strategies. The mitigations reduce failure likelihood and/or severity, providing a frame for evaluating "benchmark risk," which is scored to provide a metaevaluation benchmark: BenchRisk. Higher scores indicate that benchmark users are less likely to reach an incorrect or unsupported conclusion about an LLM. All 26 scored benchmarks present significant risk within one or more of the five scored dimensions (comprehensiveness, intelligibility, consistency, correctness, and longevity), which points to important open research directions for the field of LLM benchmarking. The BenchRisk workflow allows for comparison between benchmarks; as an open-source tool, it also facilitates the identification and sharing of risks and their mitigations.

Risk Management for Mitigating Benchmark Failure Modes: BenchRisk

TL;DR

This work addresses the problem that LLM benchmarks may mislead real-world decisions due to failure modes affecting bias, variance, coverage, and user interpretation. It introduces BenchRisk, a risk-management framework inspired by NIST practices that quantifies benchmark reliability via 57 failure modes and 196 mitigations across five dimensions. The approach defines a risk metric , applies mitigations that reduce (severity) and (likelihood), and aggregates results into an open, community-driven score at benchrisk.ai. Findings reveal pervasive risk across most benchmarks, with longevity often weak, and demonstrate how an open, iterative evaluation workflow can enable benchmark authors and users to compare reliability and prioritize improvements.

Abstract

Large language model (LLM) benchmarks inform LLM use decisions (e.g., "is this LLM safe to deploy for my use case and context?"). However, benchmarks may be rendered unreliable by various failure modes that impact benchmark bias, variance, coverage, or people's capacity to understand benchmark evidence. Using the National Institute of Standards and Technology's risk management process as a foundation, this research iteratively analyzed 26 popular benchmarks, identifying 57 potential failure modes and 196 corresponding mitigation strategies. The mitigations reduce failure likelihood and/or severity, providing a frame for evaluating "benchmark risk," which is scored to provide a metaevaluation benchmark: BenchRisk. Higher scores indicate that benchmark users are less likely to reach an incorrect or unsupported conclusion about an LLM. All 26 scored benchmarks present significant risk within one or more of the five scored dimensions (comprehensiveness, intelligibility, consistency, correctness, and longevity), which points to important open research directions for the field of LLM benchmarking. The BenchRisk workflow allows for comparison between benchmarks; as an open-source tool, it also facilitates the identification and sharing of risks and their mitigations.
Paper Structure (8 sections, 7 figures, 2 tables, 1 algorithm)

This paper contains 8 sections, 7 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related work
  3. Risk Assessment with BenchRisk
  4. Results and Discussion
  5. Conclusion
  6. Website
  7. B2 Iterative Development
  8. Scored Benchmarks

Figures (7)

  • Figure 1: The five dimensions of benchmark reliability (comprehensiveness, intelligibility, consistency, correctness, and longevity) mapped to a user's decision-making process. The questions illustrate how a user determines overall reliability ("Can I rely on it?") from an interface like AILuminate (ailuminate).
  • Figure 2: Assessed reliability dimensions and their relationships. Longevity failures degrade a benchmark over time, impacting correctness, comprehensiveness, and consistency. These properties, in turn, are foundational to a user's ability to understand the results (intelligibility).
  • Figure 3: Example risk reduction calculations for two benchmarks against two different failure modes. Left (Longevity): AILuminate-1.0 gains 0.6 points by applying Mitigation 67, which reduces the likelihood of Failure Mode 46. The "severity" of failure mode 46 is not reduced by mitigation 67 because severity and likelihood are considered separately. Right (Correctness): WinoGrande and AILuminate-1.0 apply different mitigations for Failure Mode 25, earning 0.8 and 0.7 points respectively. Additional failure modes and mitigations are available at BenchRisk.ai
  • Figure 4: BenchRisk scores for 26 benchmarks normalized to 0--100, where no risk is mitigated at zero and all known risk is mitigated at 100. "*" indicates a BenchRisk author is among the authors of the scored benchmark. A strikethrough indicates benchmarks whose unique failure modes (e.g., simulator calibration) were deemed out of scope for the current failure mode list.
  • Figure 5: Exploring the properties of time-to-saturation and benchmark quality. Left: Time to saturation for selected benchmarks. The y-axis shows the percentage of the performance gap (between initial SOTA and a perfect score) that has been closed. 0% represents the SOTA at benchmark release; 100% represents a perfect score. Benchmarks with high BenchRisk longevity scores (green) show a slower saturation rate. Right: BenchRisk correctness (y-axis) and BetterBench design (x-axis) scores plotted with a regression line. The two features appear to be independent of one another.
  • ...and 2 more figures