The AI Evaluation Ecosystem
We develop a simulation architecture that combines rule-based market dynamics with LLM-driven strategic actors, building on advances in Generative Agent-Based Modeling (GABM).
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Key points
- AI evaluation shapes the decisions of model providers, users, funders, and regulators.
- We argue that designing valid benchmarks requires contextualizing design choices in the dynamics of this ecosystem of actors.
- We model benchmarks, consumer needs, and provider capabilities as vectors over a six-dimensional capability space (reasoning, coding, knowledge, safety, communication, agentic), with structural information partitions across actors.
- We find that moving from public benchmarks to private holdout benchmarks shrinks the gap between benchmark scores and user satisfaction on most benchmarks but widens it on a few, depending on where holdout weights shift scoring credit.
Sources (1)
- [1]The AI Evaluation EcosystemarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 01:58 AM
We develop a simulation architecture that combines rule-based market dynamics with LLM-driven strategic actors, building on advances in Generative Agent-Based Modeling (GABM).
AI evaluation shapes the decisions of model providers, users, funders, and regulators.
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