PrefBench benchmark shows zero-shot LLMs achieve deal rates above 0.99 but seller profits only slightly above random and far below a simple concession heuristic across 7,500 episodes.
The First Automated Negotiating Agents Competition (ANAC 2010)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
ACCEPT 2representative citing papers
A new benchmark for sequential multi-party negotiations from climate data shows no solver dominates and performance depends on game structure.
citing papers explorer
-
PrefBench: Evaluating Zero-Shot LLM Agents in Hidden-Preference Personalized Pricing Negotiations
PrefBench benchmark shows zero-shot LLMs achieve deal rates above 0.99 but seller profits only slightly above random and far below a simple concession heuristic across 7,500 episodes.
-
A Benchmark for Multi-Party Negotiation Games from Real Negotiation Data
A new benchmark for sequential multi-party negotiations from climate data shows no solver dominates and performance depends on game structure.