@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://aclanthology.org/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."
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%0 Conference Proceedings
%T LLM-as-an-Interviewer: Beyond Static Testing Through Dynamic LLM Evaluation
%A Kim, Eunsu
%A Suk, Juyoung
%A Kim, Seungone
%A Muennighoff, Niklas
%A Kim, Dongkwan
%A Oh, Alice
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F kim-etal-2025-llm-interviewer
%X 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.
%R 10.18653/v1/2025.findings-acl.1357
%U https://aclanthology.org/2025.findings-acl.1357/
%U https://doi.org/10.18653/v1/2025.findings-acl.1357
%P 26456-26493
Markdown (Informal)
[LLM-as-an-Interviewer: Beyond Static Testing Through Dynamic LLM Evaluation](https://aclanthology.org/2025.findings-acl.1357/) (Kim et al., Findings 2025)
ACL