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    <abstract>Large language models can transform project documents into software requirements, but direct generation may introduce unsupported claims and hidden inconsistencies. We present ReqLens, an interactive multi-agent NLP system that extracts candidate requirements from mixed project documents, links them to source evidence, exposes graph-based dependencies and conflicts, and generates a Software Requirements Specification (SRS) through a human-in-the-loop workflow. We evaluate ReqLens on 13 projects using direct-generation and modular baselines, as well as component ablations. ReqLens achieves the strongest overall balance across unsupported-requirement rejection, valid-requirement retention, and seeded-defect filtering. A formative study with four domain experts provided qualitative feedback on the system’s interface and review workflow. ReqLens is accompanied by code, data, an installable package, and a narrated screencast.</abstract>
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%0 Conference Proceedings
%T ReqLens: An Interactive Evidence-Grounded Multi-Agent System for Document-to-Requirements Generation
%A Chakraborty, Manojit
%A Laddhad, Pranav
%A Ghosh, Sagnik
%Y Zhang, Ningyu
%Y Wijaya, Derry
%S Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics: System Demonstrations
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-441-5
%F chakraborty-etal-2026-reqlens
%X Large language models can transform project documents into software requirements, but direct generation may introduce unsupported claims and hidden inconsistencies. We present ReqLens, an interactive multi-agent NLP system that extracts candidate requirements from mixed project documents, links them to source evidence, exposes graph-based dependencies and conflicts, and generates a Software Requirements Specification (SRS) through a human-in-the-loop workflow. We evaluate ReqLens on 13 projects using direct-generation and modular baselines, as well as component ablations. ReqLens achieves the strongest overall balance across unsupported-requirement rejection, valid-requirement retention, and seeded-defect filtering. A formative study with four domain experts provided qualitative feedback on the system’s interface and review workflow. ReqLens is accompanied by code, data, an installable package, and a narrated screencast.
%U https://aclanthology.org/2026.aacl-demo.10/
%P 74-81

Markdown (Informal)

[ReqLens: An Interactive Evidence-Grounded Multi-Agent System for Document-to-Requirements Generation](https://aclanthology.org/2026.aacl-demo.10/) (Chakraborty et al., AACL-IJCNLP 2026)

• ReqLens: An Interactive Evidence-Grounded Multi-Agent System for Document-to-Requirements Generation (Chakraborty et al., AACL-IJCNLP 2026)

ACL

• Manojit Chakraborty, Pranav Laddhad, and Sagnik Ghosh. 2026. ReqLens: An Interactive Evidence-Grounded Multi-Agent System for Document-to-Requirements Generation. In Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics: System Demonstrations, pages 74–81, Hengqin, China. Association for Computational Linguistics.