ProEval: Proactive Failure Discovery and Efficient Performance Estimation for Generative AI Evaluation
arXiv:2604.23099v2 Announce Type: replace Abstract: Evaluating generative AI models is increasingly resource-intensive due to slow inference, expensive raters, and a rapidly growing landscape of models and benchmarks. We propose ProEval, a proactive evaluation framework that leverages transfer learning to efficiently estimate performance and identify failure cases. ProEval employs pre-trained Gaussian Processes (GPs) as surrogates for the performance score function, mapping model inputs to metrics such as the severity of errors or safety violations. By framing performance estimation as Bayesia
The increasing complexity and resource demands of generative AI model evaluation necessitate more efficient and proactive methods to manage development cycles and ensure reliable performance.
Efficient evaluation methods like ProEval are crucial for accelerating the development and deployment of generative AI, reducing costs, and proactively identifying critical failure points.
The proposed ProEval framework shifts AI evaluation from reactive, resource-intensive processes to a more proactive and efficient approach leveraging transfer learning and surrogate models.
- · AI developers
- · Generative AI companies
- · AI ethics and safety auditors
- · AI accelerator manufacturers
- · Companies relying on traditional, slow AI evaluation methods
Generative AI models can be developed and deployed faster with higher confidence in their performance and safety.
This efficiency could lead to a faster pace of innovation and adoption across various industries utilizing generative AI.
Reduced evaluation costs and time might lower the barrier to entry for smaller AI research teams and startups, fostering greater competition.
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Read at arXiv cs.LG