CallBench: A Benchmark for Dual-Goal Coordination in Phone Call Assistants

Abstract:Target-oriented dialogue systems have demonstrated strong capabilities in completing user goals through interactive conversations. However, existing studies are primarily designed for single, explicit goal completion, while phone call assistants face a proxy setting that requires coordinating the device owner's explicit preset goal with the caller's implicit and dynamic goal. We introduce \textsc{CallBench}, a Chinese benchmark for evaluating dual-goal coordination in phone call assistants. \textsc{CallBench} contains 50,000 complete multi-turn phone call dialogues across six scenarios: takeout, delivery, taxi, work, life, and harassment. It covers regular presets, emergent presets, and no-preset cases, and includes diverse relations between owner-side and caller-side goals, such as alignment, complementarity, irrelevance, and conflict. We further design a preset-aware turn-level evaluation protocol covering semantic understanding, context use, active guidance, response quality, preset compliance, dialogue rhythm, and safety. Experiments on representative dialogue methods show that existing approaches still struggle with this task, highlighting the need for phone call assistants that can make reliable turn-level decisions between two independent goals under proxy constraints.

Artificial Intelligence (cs.AI)

arXiv:2607.22635 [cs.AI]

(or arXiv:2607.22635v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.22635 Focus to learn more arXiv-issued DOI via DataCite

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