<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="chakraborty-etal-2026-reqlens">
    <titleInfo>
        <title>ReqLens: An Interactive Evidence-Grounded Multi-Agent System for Document-to-Requirements Generation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Manojit</namePart>
        <namePart type="family">Chakraborty</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Pranav</namePart>
        <namePart type="family">Laddhad</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sagnik</namePart>
        <namePart type="family">Ghosh</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>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</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Ningyu</namePart>
            <namePart type="family">Zhang</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Derry</namePart>
            <namePart type="family">Wijaya</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Hengqin, China</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-441-5</identifier>
    </relatedItem>
    <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>
    <identifier type="citekey">chakraborty-etal-2026-reqlens</identifier>
    <location>
        <url>https://aclanthology.org/2026.aacl-demo.10/</url>
    </location>
    <part>
        <date>2026-11</date>
        <extent unit="page">
            <start>74</start>
            <end>81</end>
        </extent>
    </part>
</mods>
</modsCollection>

%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)

ACL