Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
AI Research
arXiv CS.AI·Jun 12·5 min read
Iterating Toward Better Search: A Two-Agent Simulation Framework for Evaluating Agentic Search Architectures in E-Commerce
TL;DR
E-commerce search systems often struggle with effective conversational interactions. A two-agent simulation framework was developed to evaluate different conversational shopping assistant architectures.
✦ Why It Matters
Engineers can leverage this framework to systematically evaluate and improve conversational agents in e-commerce applications.
Key Takeaways
- Implement rolling-window memory for faster query responses.
- Conduct systematic failure analysis to identify and fix issues.
- Evaluate the impact of different language models on performance.
- Understand varying evaluation criteria among language models.
How It Works
The framework uses a buyer agent with specific personas and missions to interact with a responder that connects to an e-commerce search API. This setup allows for controlled experiments where the buyer's behavior remains constant, enabling precise comparisons of different responder designs.