arXiv:2510.22143v3 Announce Type: replace Abstract: Existing benchmarks and training pipelines for industrial intelligent customer service (ICS) remain misaligned with real-world dialogue requirements, overemphasizing verifiable task success while under-measuring subjective service quality and realistic failure modes, leaving a gap between offline gains and deployable dialogue behavior. We close this gap with a benchmark-to-optimization loop: we first introduce OlaBench, an ICS benchmark spanning retrieval-augmented generation, workflow-based systems, and agentic settings, which evaluates serv
The rapid advancement and deployment of AI in customer service necessitates more robust and realistic benchmarks to bridge the gap between academic progress and real-world applicability.
Improving the accuracy and reliability of AI models in customer service directly impacts business efficiency, customer satisfaction, and the broader utility of conversational AI.
The introduction of a new benchmark like OlaBench will push the development of industrial intelligent customer service towards more practical and human-centric evaluation, moving beyond simple task success.
- · AI developers focused on practical applications
- · Companies deploying advanced customer service AI
- · Customers (improved service experience)
- · AI models optimized solely for verifiable task success
- · Companies relying on outdated benchmarking methods
Customer service AI systems will become more sophisticated in handling nuanced interactions and subjective quality.
Increased adoption of agentic AI systems in service roles as their performance becomes more reliably measurable.
A potential shift in focus for AI research towards real-world human interaction complexities rather than solely technical benchmarks.
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Read at arXiv cs.CL