PrefBench: Evaluating Zero-Shot LLM Agents in Hidden-Preference Personalized Pricing Negotiations
📰 ArXiv cs.AI
Learn to evaluate zero-shot LLM agents in personalized pricing negotiations using PrefBench, a simulator-based benchmark
Action Steps
- Build a PrefBench simulator to test LLM agents in hidden-preference personalized pricing negotiations
- Configure the simulator with various buyer and seller scenarios to evaluate agent performance
- Run experiments using PrefBench to assess the profitability of LLM agent decisions
- Test the ability of LLM agents to adapt to hidden preferences and bargaining traits
- Apply PrefBench to real-world pricing negotiation scenarios to evaluate its effectiveness
Who Needs to Know This
AI researchers and engineers working on LLM agents and personalized pricing negotiations can benefit from this benchmark to evaluate their models' performance
Key Insight
💡 PrefBench provides a simulator-based benchmark to evaluate LLM agents in hidden-preference personalized pricing negotiations, enabling more effective decision making
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🤖 Evaluate zero-shot LLM agents in personalized pricing negotiations with PrefBench! 📊
Key Takeaways
Learn to evaluate zero-shot LLM agents in personalized pricing negotiations using PrefBench, a simulator-based benchmark
Full Article
Title: PrefBench: Evaluating Zero-Shot LLM Agents in Hidden-Preference Personalized Pricing Negotiations
Abstract:
arXiv:2605.22855v1 Announce Type: cross Abstract: Personalized pricing negotiations are a challenging testbed for LLM agents because successful interaction does not guarantee profitable decision making. A seller may produce valid actions and close many deals while still pricing poorly when buyer willingness to pay and bargaining traits remain hidden. This paper presents PrefBench, a simulator-based benchmark for hidden-preference personalized pricing negotiations. Each episode pairs a simulated
Abstract:
arXiv:2605.22855v1 Announce Type: cross Abstract: Personalized pricing negotiations are a challenging testbed for LLM agents because successful interaction does not guarantee profitable decision making. A seller may produce valid actions and close many deals while still pricing poorly when buyer willingness to pay and bargaining traits remain hidden. This paper presents PrefBench, a simulator-based benchmark for hidden-preference personalized pricing negotiations. Each episode pairs a simulated
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