ClarQ-LLM: A Benchmark for Models Clarifying and Requesting Information in Task-Oriented Dialog
<|reference_start|>ClarQ-LLM: A Benchmark for Models Clarifying and Requesting Information in Task-Oriented Dialog: We introduce ClarQ-LLM, an evaluation framework consisting of bilingual English-Chinese conversation tasks, conversational agents and evaluation metrics, designed to serve as a strong benchmark for assessing agents' ability to ask clarification questions in task-oriented dialogues. The benchmark includes 31 different task types, each with 10 unique dialogue scenarios between information seeker and provider agents. The scenarios require the seeker to ask questions to resolve uncertainty and gather necessary information to complete tasks. Unlike traditional benchmarks that evaluate agents based on fixed dialogue content, ClarQ-LLM includes a provider conversational agent to replicate the original human provider in the benchmark. This allows both current and future seeker agents to test their ability to complete information gathering tasks through dialogue by directly interacting with our provider agent. In tests, LLAMA3.1 405B seeker agent managed a maximum success rate of only 60.05\%, showing that ClarQ-LLM presents a strong challenge for future research.<|reference_end|>
@article{gan2024clarq-llm:,
    title={ClarQ-LLM: A Benchmark for Models Clarifying and Requesting Information
  in Task-Oriented Dialog},
    author={Yujian Gan, Changling Li, Jinxia Xie, Luou Wen, Matthew Purver,
  Massimo Poesio},
    journal={arXiv preprint arXiv:2409.06097},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.06097},
    primaryClass={cs.CL}
}