Risk Management for Mitigating Benchmark Failure Modes: BenchRisk
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866908610107277312 |
|---|---|
| author | McGregor, Sean Lu, Victor Tashev, Vassil Foundjem, Armstrong Ramasethu, Aishwarya Zarkouei, Sadegh AlMahdi Kazemi Knotz, Chris Chen, Kongtao Parrish, Alicia Reuel, Anka Frase, Heather |
| author_facet | McGregor, Sean Lu, Victor Tashev, Vassil Foundjem, Armstrong Ramasethu, Aishwarya Zarkouei, Sadegh AlMahdi Kazemi Knotz, Chris Chen, Kongtao Parrish, Alicia Reuel, Anka Frase, Heather |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_21460 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Risk Management for Mitigating Benchmark Failure Modes: BenchRisk McGregor, Sean Lu, Victor Tashev, Vassil Foundjem, Armstrong Ramasethu, Aishwarya Zarkouei, Sadegh AlMahdi Kazemi Knotz, Chris Chen, Kongtao Parrish, Alicia Reuel, Anka Frase, Heather Software Engineering Computers and Society Machine Learning 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. |
| title | Risk Management for Mitigating Benchmark Failure Modes: BenchRisk |
| topic | Software Engineering Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2510.21460 |