Dual use issues in the field of Natural Language Generation

TL;DR

This paper reports an exploratory survey within the SIGGEN community to map dual-use issues in Natural Language Generation (NLG). It describes the methodology (Qualtrics, four ACL-based questions) and presents a qualitative synthesis across perceived adverse effects, mitigation strategies, and regulatory awareness, drawing on 23 responses. Key contributions include a four-pronged mitigation framework (awareness, research, technical safeguards, regulation) and concrete ideas for raising regulatory and ethical awareness within the community. The work highlights the breadth of potential harms—from misinformation and impersonation to environmental impact and academic integrity—while outlining actionable next steps, such as focus groups and community-driven guidelines, to foster responsible NLG research and deployment.

Abstract

This report documents the results of a recent survey in the SIGGEN community, focusing on Dual Use issues in Natural Language Generation (NLG). SIGGEN is the Special Interest Group (SIG) of the Association for Computational Linguistics (ACL) for researchers working on NLG. The survey was prompted by the ACL executive board, which asked all SIGs to provide an overview of dual use issues within their respective subfields. The survey was sent out in October 2024 and the results were processed in January 2025. With 23 respondents, the survey is presumably not representative of all SIGGEN members, but at least this document offers a helpful resource for future discussions. This report is open to feedback from the SIGGEN community. Let me know if you have any questions or comments!