Service chatbots increasingly rely on LLM agents for rule-guided support and information access, but emotionally demanding complaint handling still depends heavily on human agents. To explore whether LLM agents can take on this work, researchers introduce ServiceBench, a benchmark for simulating multi-turn complaint handling under information asymmetry. The setup reflects real-world service failures where customers and agents hold different information.
The paper, posted on arXiv, positions ServiceBench as a tool for evaluating agents in these challenging interactions. The abstract notes that the benchmark is designed to address the gap between routine support and emotionally charged complaints. No experimental results are mentioned in the abstract, so the contribution at this stage is the benchmark itself.