AI model evaluations are becoming prohibitively expensive, with recent benchmarks costing tens of thousands of dollars and consuming thousands of GPU hours. This high cost is particularly pronounced for agent-based evaluations, which are inherently more complex and sensitive to setup variations. While methods exist to reduce the cost of static benchmarks through subsampling, these techniques are less effective for the dynamic and noisy nature of agent evaluations, creating a bottleneck for research and development. AI
影响 The escalating cost of AI evaluations may slow down research and development, potentially concentrating cutting-edge model assessment within well-funded organizations.
排序理由 The article discusses the rising costs and computational requirements for evaluating AI models, particularly agent-based systems, citing specific benchmark costs and research papers.
- AI21
- BLOOM
- EleutherAI
- Exgentic
- Granite-13B
- Holistic Agent Leaderboard
- Hugging Face
- IBM Research
- LM Evaluation Harness
- MMLU
- OpenAI
- Pythia
- Stanford
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