@inproceedings{kim-etal-2025-llm-interviewer,
    title = "{LLM}-as-an-Interviewer: Beyond Static Testing Through Dynamic {LLM} Evaluation",
    author = "Kim, Eunsu  and
      Suk, Juyoung  and
      Kim, Seungone  and
      Muennighoff, Niklas  and
      Kim, Dongkwan  and
      Oh, Alice",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/transition-to-people-yaml/2025.findings-acl.1357/",
    doi = "10.18653/v1/2025.findings-acl.1357",
    pages = "26456--26493",
    ISBN = "979-8-89176-256-5",
    abstract = "We introduce LLM-as-an-Interviewer, a novel paradigm for evaluating large language models (LLMs). This approach leverages multi-turn interactions where the LLM interviewer actively provides feedback on responses and poses follow-up questions to the evaluated LLM. At the start of the interview, the LLM interviewer dynamically modifies datasets to generate initial questions, mitigating data contamination. We apply the LLM-as-an-Interviewer framework to evaluate six models on the reasoning, factuality and instruction-following tasks. Our results show that the framework effectively provides insights into LLM performance, including the quality of initial responses, adaptability to feedback, and ability to address follow-up queries like clarification or additional knowledge requests. The framework also addresses key limitations of conventional methods like LLM-as-a-Judge, including verbosity bias and inconsistency across runs. Finally, we propose the Interview Report, which aggregates insights from the interview process, providing examples and a comprehensive analysis of the LLM{'}s strengths and weaknesses. This report offers a detailed snapshot of the model{'}s real-world applicability."
}