Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·Jun 12·5 min read
Token Complexity Theory for AI-Augmented Computing
TL;DR
AI-augmented computing faces a challenge in measuring the resource costs associated with querying AI models. Token complexity is introduced as a formal measure of the expected token cost to achieve desired output quality.
✦ Why It Matters
Engineers can use token complexity to better estimate resource costs for AI model interactions, improving system efficiency.
Key Takeaways
- Implement token complexity measures in your AI evaluations.
- Analyze the cost-effectiveness of different AI models using this framework.
- Explore the implications of monotonicity and convexity in your projects.
- Utilize the complexity frontier to set realistic resource expectations.
How It Works
Token complexity is defined as the minimum expected token cost to achieve a specified output quality. It is modeled using AI-Oracle Turing machines, which facilitate interactions between probabilistic machines and stochastic oracles.
This allows for a formal analysis of the costs associated with query-response exchanges in AI systems.