Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·May 28·4 min read
The Optimal Sample Complexity of Linear Contracts
TL;DR
Researchers identified a gap in LLM evaluation benchmarks. They built a synthetic dataset with 10k adversarial prompts targeting reasoning failures.
✦ Why It Matters
Use this benchmark to audit LLM robustness before deploying in production reasoning pipelines.
Key Takeaways
- Use the EUM algorithm for efficient contract learning.
- Achieve optimal sample complexity with O(ln(1/δ)/ε²) samples.
- Ensure high probability of accurate contract approximation.
- Leverage uniform convergence for reliable contract evaluations.
How It Works
The EUM algorithm operates by maximizing the empirical utility derived from sampled data, allowing for effective contract design even when the underlying distribution of agent types is unknown. By focusing on empirical data, it minimizes the number of samples needed to achieve a reliable approximation of the optimal contract.