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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2407.16711 |
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| _version_ | 1866911971376365568 |
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| author | Saxon, Michael Holtzman, Ari West, Peter Wang, William Yang Saphra, Naomi |
| author_facet | Saxon, Michael Holtzman, Ari West, Peter Wang, William Yang Saphra, Naomi |
| contents | Modern language models (LMs) pose a new challenge in capability assessment. Static benchmarks inevitably saturate without providing confidence in the deployment tolerances of LM-based systems, but developers nonetheless claim that their models have generalized traits such as reasoning or open-domain language understanding based on these flawed metrics. The science and practice of LMs requires a new approach to benchmarking which measures specific capabilities with dynamic assessments. To be confident in our metrics, we need a new discipline of model metrology -- one which focuses on how to generate benchmarks that predict performance under deployment. Motivated by our evaluation criteria, we outline how building a community of model metrology practitioners -- one focused on building tools and studying how to measure system capabilities -- is the best way to meet these needs to and add clarity to the AI discussion. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_16711 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Benchmarks as Microscopes: A Call for Model Metrology Saxon, Michael Holtzman, Ari West, Peter Wang, William Yang Saphra, Naomi Software Engineering Computation and Language Modern language models (LMs) pose a new challenge in capability assessment. Static benchmarks inevitably saturate without providing confidence in the deployment tolerances of LM-based systems, but developers nonetheless claim that their models have generalized traits such as reasoning or open-domain language understanding based on these flawed metrics. The science and practice of LMs requires a new approach to benchmarking which measures specific capabilities with dynamic assessments. To be confident in our metrics, we need a new discipline of model metrology -- one which focuses on how to generate benchmarks that predict performance under deployment. Motivated by our evaluation criteria, we outline how building a community of model metrology practitioners -- one focused on building tools and studying how to measure system capabilities -- is the best way to meet these needs to and add clarity to the AI discussion. |
| title | Benchmarks as Microscopes: A Call for Model Metrology |
| topic | Software Engineering Computation and Language |
| url | https://arxiv.org/abs/2407.16711 |