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HotelQuEST: Balancing Quality and Efficiency in Agentic Search
arXiv – CS AI|Guy Hadad, Shadi Iskander, Oren Kalinsky, Sofia Tolmach, Ran Levy, Haggai Roitman||7 views
🤖AI Summary
Researchers introduce HotelQuEST, a new benchmark for evaluating agentic search systems that balances quality and efficiency metrics. The study reveals that while LLM-based agents achieve higher accuracy than traditional retrievers, they incur substantially higher costs due to redundant operations and poor optimization.
Key Takeaways
- →HotelQuEST benchmark includes 214 hotel search queries ranging from simple to complex for comprehensive evaluation.
- →Current agentic search benchmarks focus primarily on quality while overlooking critical efficiency factors for real-world deployment.
- →LLM-based agents demonstrate higher accuracy than traditional retrievers but at significantly higher operational costs.
- →Inefficiencies stem from redundant tool calls and suboptimal routing that fails to match query complexity to model capability.
- →The research identifies substantial potential for cost-aware optimization in agentic search systems.
Read Original →via arXiv – CS AI
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