TUM-Heilbronn: WMT26 Multilingual Instruction Shared Task

Jen-Chien Hou; Tsedeniya Kinfe Temesgen; Alexander Fraser

TUM-Heilbronn: WMT26 Multilingual Instruction Shared Task

Jen-Chien Hou, Tsedeniya Kinfe Temesgen, Alexander Fraser

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 PDF.)

Authors concatenated from the text boxes above:

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

Abstract

This paper describes the TUM-Heilbronn sys- tem submission to the WMT26 Multilingual Instruction Shared Task (MIST). The MIST covers three tasks: context-based question an- swering, open-ended generation, and cross- lingual summarization to evaluate the multi- lingual instruction-following ability of large language models. We fine-tune Qwen3.5-9B- Instruct on 27 diverse languages, across 10 dif- ferent writing scripts. Our model outperformed 6 of the 15 models submitted to this shared task. Moreover, we ranked first in truthfulness (i.e., decline to answer when the context lacks sufficient information) on the context-based question-answering task. Cross-lingual sum- marization remains a challenging task for all submitted models, with the lowest performance observed among the three tasks.

Anthology ID:: 2026.wmt-main.128
Volume:: Proceedings of the Eleventh Conference on Machine Translation
Month:: October
Year:: 2026
Address:: Budapest, Hungary
Editors:: Barry Haddow, Tom Kocmi, Philipp Koehn, Christof Monz
Venues:: WMT | WS
SIG:: SIGMT
Publisher:: Association for Computational Linguistics
Note:
Pages:: 1925–1932
Language:
URL:: https://aclanthology.org/2026.wmt-main.128/
DOI:
Bibkey:
Cite (ACL):: Jen-Chien Hou, Tsedeniya Kinfe Temesgen, and Alexander Fraser. 2026. TUM-Heilbronn: WMT26 Multilingual Instruction Shared Task. In Proceedings of the Eleventh Conference on Machine Translation, pages 1925–1932, Budapest, Hungary. Association for Computational Linguistics.
Cite (Informal):: TUM-Heilbronn: WMT26 Multilingual Instruction Shared Task (Hou et al., WMT 2026)
Copy Citation:
PDF:: https://aclanthology.org/2026.wmt-main.128.pdf

PDF Cite Search Fix data

Export citation

BibTeX
MODS XML
Endnote
Preformatted

@inproceedings{hou-etal-2026-tum,
    title = "{TUM}-Heilbronn: {WMT}26 Multilingual Instruction Shared Task",
    author = "Hou, Jen-Chien  and
      Temesgen, Tsedeniya Kinfe  and
      Fraser, Alexander",
    editor = "Haddow, Barry  and
      Kocmi, Tom  and
      Koehn, Philipp  and
      Monz, Christof",
    booktitle = "Proceedings of the Eleventh Conference on Machine Translation",
    month = oct,
    year = "2026",
    address = "Budapest, Hungary",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.wmt-main.128/",
    pages = "1925--1932",
    ISBN = "979-8-89176-474-3",
    abstract = "This paper describes the TUM-Heilbronn sys- tem submission to the WMT26 Multilingual Instruction Shared Task (MIST). The MIST covers three tasks: context-based question an- swering, open-ended generation, and cross- lingual summarization to evaluate the multi- lingual instruction-following ability of large language models. We fine-tune Qwen3.5-9B- Instruct on 27 diverse languages, across 10 dif- ferent writing scripts. Our model outperformed 6 of the 15 models submitted to this shared task. Moreover, we ranked first in truthfulness (i.e., decline to answer when the context lacks sufficient information) on the context-based question-answering task. Cross-lingual sum- marization remains a challenging task for all submitted models, with the lowest performance observed among the three tasks."
}

Download as File

<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="hou-etal-2026-tum">
    <titleInfo>
        <title>TUM-Heilbronn: WMT26 Multilingual Instruction Shared Task</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Jen-Chien</namePart>
        <namePart type="family">Hou</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Tsedeniya</namePart>
        <namePart type="given">Kinfe</namePart>
        <namePart type="family">Temesgen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Alexander</namePart>
        <namePart type="family">Fraser</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-10</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Eleventh Conference on Machine Translation</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Barry</namePart>
            <namePart type="family">Haddow</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Tom</namePart>
            <namePart type="family">Kocmi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Philipp</namePart>
            <namePart type="family">Koehn</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Christof</namePart>
            <namePart type="family">Monz</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Budapest, Hungary</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-474-3</identifier>
    </relatedItem>
    <abstract>This paper describes the TUM-Heilbronn sys- tem submission to the WMT26 Multilingual Instruction Shared Task (MIST). The MIST covers three tasks: context-based question an- swering, open-ended generation, and cross- lingual summarization to evaluate the multi- lingual instruction-following ability of large language models. We fine-tune Qwen3.5-9B- Instruct on 27 diverse languages, across 10 dif- ferent writing scripts. Our model outperformed 6 of the 15 models submitted to this shared task. Moreover, we ranked first in truthfulness (i.e., decline to answer when the context lacks sufficient information) on the context-based question-answering task. Cross-lingual sum- marization remains a challenging task for all submitted models, with the lowest performance observed among the three tasks.</abstract>
    <identifier type="citekey">hou-etal-2026-tum</identifier>
    <location>
        <url>https://aclanthology.org/2026.wmt-main.128/</url>
    </location>
    <part>
        <date>2026-10</date>
        <extent unit="page">
            <start>1925</start>
            <end>1932</end>
        </extent>
    </part>
</mods>
</modsCollection>

Download as File

%0 Conference Proceedings
%T TUM-Heilbronn: WMT26 Multilingual Instruction Shared Task
%A Hou, Jen-Chien
%A Temesgen, Tsedeniya Kinfe
%A Fraser, Alexander
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Eleventh Conference on Machine Translation
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-474-3
%F hou-etal-2026-tum
%X This paper describes the TUM-Heilbronn sys- tem submission to the WMT26 Multilingual Instruction Shared Task (MIST). The MIST covers three tasks: context-based question an- swering, open-ended generation, and cross- lingual summarization to evaluate the multi- lingual instruction-following ability of large language models. We fine-tune Qwen3.5-9B- Instruct on 27 diverse languages, across 10 dif- ferent writing scripts. Our model outperformed 6 of the 15 models submitted to this shared task. Moreover, we ranked first in truthfulness (i.e., decline to answer when the context lacks sufficient information) on the context-based question-answering task. Cross-lingual sum- marization remains a challenging task for all submitted models, with the lowest performance observed among the three tasks.
%U https://aclanthology.org/2026.wmt-main.128/
%P 1925-1932

Download as File

Markdown (Informal)

[TUM-Heilbronn: WMT26 Multilingual Instruction Shared Task](https://aclanthology.org/2026.wmt-main.128/) (Hou et al., WMT 2026)

TUM-Heilbronn: WMT26 Multilingual Instruction Shared Task (Hou et al., WMT 2026)

ACL

Jen-Chien Hou, Tsedeniya Kinfe Temesgen, and Alexander Fraser. 2026. TUM-Heilbronn: WMT26 Multilingual Instruction Shared Task. In Proceedings of the Eleventh Conference on Machine Translation, pages 1925–1932, Budapest, Hungary. Association for Computational Linguistics.