PrefBench: Evaluating Zero-Shot LLM Agents in Hidden-Preference Personalized Pricing Negotiations

1. 1Build a PrefBench simulator to test LLM agents in hidden-preference personalized pricing negotiations
2. 2Configure the simulator with various buyer and seller scenarios to evaluate agent performance
3. 3Run experiments using PrefBench to assess the profitability of LLM agent decisions
4. 4Test the ability of LLM agents to adapt to hidden preferences and bargaining traits
5. 5Apply PrefBench to real-world pricing negotiation scenarios to evaluate its effectiveness

AI researchers and engineers working on LLM agents and personalized pricing negotiations can benefit from this benchmark to evaluate their models' performance

💡 PrefBench provides a simulator-based benchmark to evaluate LLM agents in hidden-preference personalized pricing negotiations, enabling more effective decision making

🤖 Evaluate zero-shot LLM agents in personalized pricing negotiations with PrefBench! 📊