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    <abstract>Building upon the critical importance of ethical considerations in research, we introduce a novel task of Ethical Questionnaire Generation (EQG) for research papers. Ethical review has become an indispensable component of the research process, helping identify potential risks, biases, and societal impacts that may arise from scientific work. In this paper, we present EthiQuest, a comprehensive dataset comprising 3663 research papers paired with their corresponding ethical questionnaires extracted from major conference proceedings. We explore various approaches leveraging large language models (LLMs) to automatically generate context-aware ethical questionnaires, examining the unique challenges of capturing domain-specific ethical concerns, ensuring comprehensive coverage of potential issues, and maintaining question relevance and clarity. Our experiments demonstrate the effectiveness of fine-tuned LLMs in generating pertinent ethical questions across diverse research domains. We provide detailed analysis of question quality, coverage metrics, and practical insights for deploying such systems in real-world research review processes. The EQG dataset and code can be accessed at https://anonymous.4open.science/r/eqg-979C/.</abstract>
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%0 Conference Proceedings
%T EthiQuest: LLM-Powered Ethical Questionnaire Generation for Research Review
%A Kapania, Ishank
%A Mamidi, Radhika
%A Mishra, Rahul
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F kapania-etal-2026-ethiquest
%X Building upon the critical importance of ethical considerations in research, we introduce a novel task of Ethical Questionnaire Generation (EQG) for research papers. Ethical review has become an indispensable component of the research process, helping identify potential risks, biases, and societal impacts that may arise from scientific work. In this paper, we present EthiQuest, a comprehensive dataset comprising 3663 research papers paired with their corresponding ethical questionnaires extracted from major conference proceedings. We explore various approaches leveraging large language models (LLMs) to automatically generate context-aware ethical questionnaires, examining the unique challenges of capturing domain-specific ethical concerns, ensuring comprehensive coverage of potential issues, and maintaining question relevance and clarity. Our experiments demonstrate the effectiveness of fine-tuned LLMs in generating pertinent ethical questions across diverse research domains. We provide detailed analysis of question quality, coverage metrics, and practical insights for deploying such systems in real-world research review processes. The EQG dataset and code can be accessed at https://anonymous.4open.science/r/eqg-979C/.
%R 10.63317/3oju9vtxpezh
%U https://aclanthology.org/2026.lrec-1.96/
%U https://doi.org/10.63317/3oju9vtxpezh
%P 1216-1225

Markdown (Informal)

[EthiQuest: LLM-Powered Ethical Questionnaire Generation for Research Review](https://aclanthology.org/2026.lrec-1.96/) (Kapania et al., LREC 2026)

• EthiQuest: LLM-Powered Ethical Questionnaire Generation for Research Review (Kapania et al., LREC 2026)

ACL

• Ishank Kapania, Radhika Mamidi, and Rahul Mishra. 2026. EthiQuest: LLM-Powered Ethical Questionnaire Generation for Research Review. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 1216–1225, Palma de Mallorca, Spain. ELRA Language Resource Association.