LFQA-HP-1M: A Large-Scale Human Preference Dataset for Long-Form Question Answering

Rafid Ishrak Jahan; Fahmid Shahriar Iqbal; Sagnik Ray Choudhury

doi:10.63317/2oq3nh8a3zff

LFQA-HP-1M: A Large-Scale Human Preference Dataset for Long-Form Question Answering

Rafid Ishrak Jahan, Fahmid Shahriar Iqbal, Sagnik Ray Choudhury

Correct Metadata for

Use this form to create a GitHub issue with structured data describing the correction. You will need a GitHub account. Once you create that issue, the correction will be reviewed by a staff member.

⚠️ Mobile Users: Submitting this form to create a new issue will only work with github.com, not the GitHub Mobile app.

Important: The Anthology treat PDFs as authoritative. Please use this form only to correct data that is out of line with the PDF. See our corrections guidelines if you need to change the PDF.

Title Adjust the title. Retain tags such as <fixed-case>.

Authors Adjust author names and order to match the PDF.

Abstract Correct abstract if needed. Retain XML formatting tags such as <tex-math>. You may use ... for bold, ... for italic, ... for underline, <sc>...</sc> for small-caps, <tt>...<tt> for typewriter text, <url>...</url> for URLs, <a href=...> for hyperlinks, and <par/> for paragraph breaks.

Verification against PDF Ensure that the new title/authors match the snapshot below. (If there is no snapshot or it is too small, consult the external publication .)

Authors concatenated from the text boxes above:

ALL author names match the snapshot above—including middle initials, hyphens, and accents.

Abstract

Long-form question answering (LFQA) demands nuanced evaluation of multi-sentence explanatory responses, yet existing metrics often fail to reflect human judgment. We present LFQA-HP-1M, a large-scale dataset comprising 1.3M human pairwise preference annotations for LFQA. We propose nine rubrics for answer quality evaluation, and show that simple linear models based on these features perform comparably to state-of-the-art LLM evaluators. We further examine transitivity consistency, positional bias, and verbosity biases in LLM evaluators and demonstrate their vulnerability to adversarial perturbations. Overall, this work provides one of the largest public LFQA preference datasets and a rubric-driven framework for transparent and reliable evaluation.

Anthology ID:: 2026.lrec-1.425
Volume:: Proceedings of the Fifteenth Language Resources and Evaluation Conference
Month:: May
Year:: 2026
Address:: Palma de Mallorca, Spain
Editors:: Stelios Piperidis, Núria Bel, Henk van den Heuvel, Nancy Ide, Simon Krek, Antonio Toral
Venue:: LREC
SIG:
Publisher:: ELRA Language Resource Association
Note:
Pages:: 5450–5465
Language:
External URL:: https://lrec.elra.info/lrec2026-main-425
DOI:: 10.63317/2oq3nh8a3zff
Bibkey:
Cite (ACL):: Rafid Ishrak Jahan, Fahmid Shahriar Iqbal, and Sagnik Ray Choudhury. 2026. LFQA-HP-1M: A Large-Scale Human Preference Dataset for Long-Form Question Answering. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 5450–5465, Palma de Mallorca, Spain. ELRA Language Resource Association.
Cite (Informal):: LFQA-HP-1M: A Large-Scale Human Preference Dataset for Long-Form Question Answering (Jahan et al., LREC 2026)
Copy Citation:

External Cite Search Fix data

Export citation

BibTeX
MODS XML
Endnote
Preformatted

@inproceedings{jahan-etal-2026-lfqa,
    title = "{LFQA}-{HP}-1{M}: A Large-Scale Human Preference Dataset for Long-Form Question Answering",
    author = "Jahan, Rafid Ishrak  and
      Iqbal, Fahmid Shahriar  and
      Ray Choudhury, Sagnik",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://aclanthology.org/2026.lrec-1.425/",
    doi = "10.63317/2oq3nh8a3zff",
    pages = "5450--5465",
    abstract = "Long-form question answering (LFQA) demands nuanced evaluation of multi-sentence explanatory responses, yet existing metrics often fail to reflect human judgment. We present LFQA-HP-1M, a large-scale dataset comprising 1.3M human pairwise preference annotations for LFQA. We propose nine rubrics for answer quality evaluation, and show that simple linear models based on these features perform comparably to state-of-the-art LLM evaluators. We further examine transitivity consistency, positional bias, and verbosity biases in LLM evaluators and demonstrate their vulnerability to adversarial perturbations. Overall, this work provides one of the largest public LFQA preference datasets and a rubric-driven framework for transparent and reliable evaluation."
}

Download as File

<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="jahan-etal-2026-lfqa">
    <titleInfo>
        <title>LFQA-HP-1M: A Large-Scale Human Preference Dataset for Long-Form Question Answering</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Rafid</namePart>
        <namePart type="given">Ishrak</namePart>
        <namePart type="family">Jahan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Fahmid</namePart>
        <namePart type="given">Shahriar</namePart>
        <namePart type="family">Iqbal</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sagnik</namePart>
        <namePart type="family">Ray Choudhury</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-05</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Fifteenth Language Resources and Evaluation Conference</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Stelios</namePart>
            <namePart type="family">Piperidis</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Núria</namePart>
            <namePart type="family">Bel</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Henk</namePart>
            <namePart type="family">van den Heuvel</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Nancy</namePart>
            <namePart type="family">Ide</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Simon</namePart>
            <namePart type="family">Krek</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Antonio</namePart>
            <namePart type="family">Toral</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>ELRA Language Resource Association</publisher>
            <place>
                <placeTerm type="text">Palma de Mallorca, Spain</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Long-form question answering (LFQA) demands nuanced evaluation of multi-sentence explanatory responses, yet existing metrics often fail to reflect human judgment. We present LFQA-HP-1M, a large-scale dataset comprising 1.3M human pairwise preference annotations for LFQA. We propose nine rubrics for answer quality evaluation, and show that simple linear models based on these features perform comparably to state-of-the-art LLM evaluators. We further examine transitivity consistency, positional bias, and verbosity biases in LLM evaluators and demonstrate their vulnerability to adversarial perturbations. Overall, this work provides one of the largest public LFQA preference datasets and a rubric-driven framework for transparent and reliable evaluation.</abstract>
    <identifier type="citekey">jahan-etal-2026-lfqa</identifier>
    <identifier type="doi">10.63317/2oq3nh8a3zff</identifier>
    <location>
        <url>https://aclanthology.org/2026.lrec-1.425/</url>
    </location>
    <part>
        <date>2026-05</date>
        <extent unit="page">
            <start>5450</start>
            <end>5465</end>
        </extent>
    </part>
</mods>
</modsCollection>

Download as File

%0 Conference Proceedings
%T LFQA-HP-1M: A Large-Scale Human Preference Dataset for Long-Form Question Answering
%A Jahan, Rafid Ishrak
%A Iqbal, Fahmid Shahriar
%A Ray Choudhury, Sagnik
%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 jahan-etal-2026-lfqa
%X Long-form question answering (LFQA) demands nuanced evaluation of multi-sentence explanatory responses, yet existing metrics often fail to reflect human judgment. We present LFQA-HP-1M, a large-scale dataset comprising 1.3M human pairwise preference annotations for LFQA. We propose nine rubrics for answer quality evaluation, and show that simple linear models based on these features perform comparably to state-of-the-art LLM evaluators. We further examine transitivity consistency, positional bias, and verbosity biases in LLM evaluators and demonstrate their vulnerability to adversarial perturbations. Overall, this work provides one of the largest public LFQA preference datasets and a rubric-driven framework for transparent and reliable evaluation.
%R 10.63317/2oq3nh8a3zff
%U https://aclanthology.org/2026.lrec-1.425/
%U https://doi.org/10.63317/2oq3nh8a3zff
%P 5450-5465

Download as File

Markdown (Informal)

[LFQA-HP-1M: A Large-Scale Human Preference Dataset for Long-Form Question Answering](https://aclanthology.org/2026.lrec-1.425/) (Jahan et al., LREC 2026)

LFQA-HP-1M: A Large-Scale Human Preference Dataset for Long-Form Question Answering (Jahan et al., LREC 2026)

ACL

Rafid Ishrak Jahan, Fahmid Shahriar Iqbal, and Sagnik Ray Choudhury. 2026. LFQA-HP-1M: A Large-Scale Human Preference Dataset for Long-Form Question Answering. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 5450–5465, Palma de Mallorca, Spain. ELRA Language Resource Association.