Risk Management for Mitigating Benchmark Failure Modes: BenchRisk

• URL: http://arxiv.org/abs/2510.21460v1
• Date: Fri, 24 Oct 2025 13:43:29 GMT
• Title: Risk Management for Mitigating Benchmark Failure Modes: BenchRisk
• Abstract summary: This research iteratively analyzed 26 popular benchmarks, identifying 57 potential failure modes and 196 corresponding mitigation strategies.<n>The mitigations reduce failure likelihood and/or severity, providing a frame for evaluating "benchmark risk"
• Score: 7.869587516646358
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

Related papers

• Benchmarking the Benchmarks: Evaluating Automated Safety Benchmarks for Small Language Models [10.539135916505133]
  Small Language Models (SLMs) are increasingly deployed in resource-constrained, privacy-sensitive settings.<n>Existing AI safetyslash securityslash compliance benchmarks are designed for large language models that may not transfer reliably to SLMs.<n>We ask: Can these benchmarks effectively and reliably evaluate SLMs?
  arXiv  Detail & Related papers  (2026-08-17T22:47:14Z)
• Taxonomy and Consistency Analysis of Safety Benchmarks for AI Agents [6.787194586338237]
  We present the first systematic analysis dedicated to agent safety benchmarks as evaluation instruments.<n>A coverage matrix reveals broad risk coverage but limited methodological convergence, while the taxonomy analysis shows a behavioral-benchmark core concentrated in sandboxed, constrained, and often safety-only evaluation.<n>Across the landscape, we find that benchmark choice can yield contradictory safety conclusions, coverage counts often overstate evaluation depth, environment fidelity shapes reported safety, the field disproportionately tests externally imposed rather than agent-internal risks, metric fragmentation limits comparison, and remains effectively unbenchmarked.
  arXiv  Detail & Related papers  (2026-04-11T04:25:19Z)
• DEP: A Decentralized Large Language Model Evaluation Protocol [51.3646001384887]
  Decentralized Evaluation Protocol (DEP) is a decentralized yet unified and standardized evaluation framework.<n>By decoupling users, LLMs, and benchmarks, DEP enables modular, plug-and-play evaluation.<n>We develop DEP Toolkit, a protocol-compatible toolkit that supports features such as breakpoint resume, concurrent requests, and congestion control.
  arXiv  Detail & Related papers  (2026-03-01T16:10:16Z)
• How Much Do Large Language Model Cheat on Evaluation? Benchmarking Overestimation under the One-Time-Pad-Based Framework [8.76693832650115]
  Overestimation in evaluating large language models (LLMs) has become an increasing concern.<n>We propose ArxivRoll, a dynamic evaluation framework inspired by one-time pad encryption in cryptography.
  arXiv  Detail & Related papers  (2025-07-25T12:39:03Z)
• FLEX: A Benchmark for Evaluating Robustness of Fairness in Large Language Models [7.221774553388335]
  We introduce a new benchmark to test whether Large Language Models can sustain fairness even when exposed to prompts constructed to induce bias.<n>We integrate prompts that amplify potential biases into the fairness assessment.<n>This highlights the need for more stringent evaluation benchmarks to guarantee safety and fairness.
  arXiv  Detail & Related papers  (2025-03-25T10:48:33Z)
• PredictaBoard: Benchmarking LLM Score Predictability [50.47497036981544]
  Large Language Models (LLMs) often fail unpredictably.<n>This poses a significant challenge to ensuring their safe deployment.<n>We present PredictaBoard, a novel collaborative benchmarking framework.
  arXiv  Detail & Related papers  (2025-02-20T10:52:38Z)
• Beyond the Singular: The Essential Role of Multiple Generations in Effective Benchmark Evaluation and Analysis [10.133537818749291]
  Large language models (LLMs) have demonstrated significant utilities in real-world applications.<n> Benchmark evaluations are crucial for assessing the capabilities of LLMs.
  arXiv  Detail & Related papers  (2025-02-13T03:43:33Z)
• BenchmarkCards: Standardized Documentation for Large Language Model Benchmarks [23.263430784766026]
  Large language models (LLMs) are powerful tools capable of handling diverse tasks.<n>Finding suitable benchmarks is difficult given the many available options.<n>We introduce textttBenchmarkCards, an intuitive and validated documentation framework.
  arXiv  Detail & Related papers  (2024-10-16T19:09:02Z)
• SeCodePLT: A Unified Platform for Evaluating the Security of Code GenAI [58.29510889419971]
  Existing benchmarks for evaluating the security risks and capabilities of code-generating large language models (LLMs) face several key limitations.<n>We introduce a general and scalable benchmark construction framework that begins with manually validated, high-quality seed examples and expands them via targeted mutations.<n>Applying this framework to Python, C/C++, and Java, we build SeCodePLT, a dataset of more than 5.9k samples spanning 44 CWE-based risk categories and three security capabilities.
  arXiv  Detail & Related papers  (2024-10-14T21:17:22Z)
• Inference-Time Decontamination: Reusing Leaked Benchmarks for Large Language Model Evaluation [61.350306618479365]
  Leakage of benchmarks can prevent the accurate assessment of large language models' true performance. We propose Inference-Time Decontamination (ITD) to address this issue. ITD reduces inflated accuracy by 22.9% on GSM8K and 19.0% on MMLU.
  arXiv  Detail & Related papers  (2024-06-20T04:35:59Z)
• MR-Ben: A Meta-Reasoning Benchmark for Evaluating System-2 Thinking in LLMs [55.20845457594977]
  Large language models (LLMs) have shown increasing capability in problem-solving and decision-making.<n>We present a process-based benchmark MR-Ben that demands a meta-reasoning skill.<n>Our meta-reasoning paradigm is especially suited for system-2 slow thinking.
  arXiv  Detail & Related papers  (2024-06-20T03:50:23Z)
• The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models [94.31327813151208]
  BiGGen Bench is a principled generation benchmark designed to thoroughly evaluate nine distinct capabilities of LMs across 77 diverse tasks.<n>A key feature of the BiGGen Bench is its use of instance-specific evaluation criteria, closely mirroring the nuanced discernment of human evaluation.
  arXiv  Detail & Related papers  (2024-06-09T12:30:30Z)
• TRUCE: Private Benchmarking to Prevent Contamination and Improve Comparative Evaluation of LLMs [12.839640915518443]
  Benchmarking is the de-facto standard for evaluating LLMs, due to its speed, replicability and low cost. Recent work has pointed out that the majority of the open source benchmarks available today have been contaminated or leaked into LLMs. We propose Private Benchmarking, a solution where test datasets are kept private and models are evaluated without revealing the test data to the model.
  arXiv  Detail & Related papers  (2024-03-01T09:28:38Z)
• Efficient Benchmarking of Language Models [22.696230279151166]
  We present the problem of Efficient Benchmarking, namely, intelligently reducing the costs of LM evaluation without compromising reliability. Using the HELM benchmark as a test case, we investigate how different benchmark design choices affect the computation-reliability trade-off. We propose an evaluation algorithm, that, when applied to the HELM benchmark, leads to dramatic cost savings with minimal loss of benchmark reliability.
  arXiv  Detail & Related papers  (2023-08-22T17:59:30Z)
• LLMs as Factual Reasoners: Insights from Existing Benchmarks and Beyond [135.8013388183257]
  We propose a new protocol for inconsistency detection benchmark creation and implement it in a 10-domain benchmark called SummEdits. Most LLMs struggle on SummEdits, with performance close to random chance. The best-performing model, GPT-4, is still 8% below estimated human performance.
  arXiv  Detail & Related papers  (2023-05-23T21:50:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.