@inproceedings{pulipaka-2026-psk-wmt,
title = "{PSK} at {WMT} 2026 {MIST}: Task-Specialized {QL}o{RA} Adapters for Multilingual Summarization and Question Answering",
author = "Pulipaka, Srikar Kashyap",
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.131/",
pages = "1959--1964",
ISBN = "979-8-89176-474-3",
abstract = "We describe the PSK submission to the WMT 2026 Multilingual Instruction Shared Task. Our system uses the 3.35B-parameter Tiny Aya Global model with three QLoRA adapters, one for each task. The adapters are trained on multilingual document{--}summary pairs, passage-based question answering, and filtered standalone question answering. The summarization data also includes scientific papers with their author-written abstracts. On our held-out split, the context and summarization adapters perform better than our multitask adapter, while results for open QA are mixed. Official evaluation ranks PSK sixth overall and fourth for summarization. Context QA and open QA remain weaker, and the multitask open-QA route outperforms both specialized open-QA adapters."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="pulipaka-2026-psk-wmt">
<titleInfo>
<title>PSK at WMT 2026 MIST: Task-Specialized QLoRA Adapters for Multilingual Summarization and Question Answering</title>
</titleInfo>
<name type="personal">
<namePart type="given">Srikar</namePart>
<namePart type="given">Kashyap</namePart>
<namePart type="family">Pulipaka</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>We describe the PSK submission to the WMT 2026 Multilingual Instruction Shared Task. Our system uses the 3.35B-parameter Tiny Aya Global model with three QLoRA adapters, one for each task. The adapters are trained on multilingual document–summary pairs, passage-based question answering, and filtered standalone question answering. The summarization data also includes scientific papers with their author-written abstracts. On our held-out split, the context and summarization adapters perform better than our multitask adapter, while results for open QA are mixed. Official evaluation ranks PSK sixth overall and fourth for summarization. Context QA and open QA remain weaker, and the multitask open-QA route outperforms both specialized open-QA adapters.</abstract>
<identifier type="citekey">pulipaka-2026-psk-wmt</identifier>
<location>
<url>https://aclanthology.org/2026.wmt-main.131/</url>
</location>
<part>
<date>2026-10</date>
<extent unit="page">
<start>1959</start>
<end>1964</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T PSK at WMT 2026 MIST: Task-Specialized QLoRA Adapters for Multilingual Summarization and Question Answering
%A Pulipaka, Srikar Kashyap
%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 pulipaka-2026-psk-wmt
%X We describe the PSK submission to the WMT 2026 Multilingual Instruction Shared Task. Our system uses the 3.35B-parameter Tiny Aya Global model with three QLoRA adapters, one for each task. The adapters are trained on multilingual document–summary pairs, passage-based question answering, and filtered standalone question answering. The summarization data also includes scientific papers with their author-written abstracts. On our held-out split, the context and summarization adapters perform better than our multitask adapter, while results for open QA are mixed. Official evaluation ranks PSK sixth overall and fourth for summarization. Context QA and open QA remain weaker, and the multitask open-QA route outperforms both specialized open-QA adapters.
%U https://aclanthology.org/2026.wmt-main.131/
%P 1959-1964
Markdown (Informal)
[PSK at WMT 2026 MIST: Task-Specialized QLoRA Adapters for Multilingual Summarization and Question Answering](https://aclanthology.org/2026.wmt-main.131/) (Pulipaka, WMT 2026)
ACL