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Risk Management for Mitigating Benchmark Failure Modes: BenchRisk
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Proposes BenchRisk, a NIST-style risk management framework that catalogs 57 benchmark failure modes and 196 mitigations, and uses them to score 26 LLM benchmarks on five reliability dimensions.
Problem
LLM benchmarks used to inform real-world deployment decisions can be unreliable due to bias, variance, coverage, longevity, and user-comprehension failures, yet users lack an efficient way to judge benchmark reliability.
Approach
Applies the NIST risk management process (failure modes, likelihood, severity, mitigations) to LLM benchmarks, scoring each benchmark's residual risk across five dimensions (comprehensiveness, intelligibility, consistency, correctness, longevity) to form a metaevaluation benchmark.
Key results
- All 26 scored benchmarks present significant risk in one or more of the five dimensions.
- Longevity is the weakest dimension for most benchmarks, with AILuminate and ARC-AGI-Private the notable high-longevity outliers.
- Inter-rater agreement on BBQ scoring across five reviewers gave Fleiss' kappa of 0.53 (moderate).
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use confidentiality practices that keep evaluation data from SUT developers | Benchmarks seeking longevity · Benchmark longevity | longevity | Helped |
| Adopt mitigation 28 to refrain from making the evaluator or ground truth publicly available | AILuminate · Benchmark longevity | longevity | Helped |
| Keep the test set from SUT developers | ARC-AGI-Private · Benchmark longevity | longevity | Helped |
| Use a compute budget comparable to earlier evaluations | OpenAI evaluation on ARC-AGI semi-private · ARC-AGI semi-private | score: 88 percent; 53 percent | Mixed |
| Compare BetterBench design scores with BenchRisk correctness scores | BenchRisk and BetterBench · Scored benchmarks | relationship between scores | No clear effect |
| Use a benchmark's human-baseline performance to assess its progression over time | Selected benchmarks · Time to saturation | performance improvement | Mixed |
| Score benchmark mitigations independently across five raters | BenchRisk BBQ assessment · BBQ benchmark scoring agreement | mitigations scored consistently: 117 | Helped |
Surprising findings
- BenchRisk scores appear independent of BetterBench design scores.
- Comprehensiveness and intelligibility are among the weakest dimensions, with 16 benchmarks unmitigated for an overly broad task definition.
Limitations
- Scores are expert judgments and not statistically derived; likelihood is subjective.
- Authors are biased toward early benchmarks and self-scoring by benchmark authors is expected to be more accurate.
- Some benchmarks (simulator-based) were scored without extending failure modes; sample for longevity-saturation analysis is small.
Experimental setup
Datasets: AILuminate AIRBench AnthropicRedTeam ARC-AGI-Private ARC-AGI-Public BBQ Big Bench Big Bench Hard Big Bench Extra Hard BOLD Bias DecodingTrustPrivacy DecodingTrustToxicity
Benchmarks: BenchRisk scores for 26 LLM benchmarks
Metrics: BenchRisk score (0-100) Fleiss' kappa
Baselines: BetterBench
Builds on: BetterBench NIST risk management framework
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