<div class="csl-bib-body">
<div class="csl-entry">Hampp, N., Mirylenka, K., & Glass, M. (2026). Optimizing Large Language Models for Robust Domain-Specific Text-to-SQL: From Prompting to Preference Alignment. In <i>Proceedings of the 11th Edition of the Swiss Text Analytics Conference</i> (pp. 64–75). Association for Computational Linguistics.</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/230647
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dc.language.iso
en
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dc.subject
LLM
en
dc.subject
Text-to-SQL
en
dc.subject
NL2SQL
en
dc.title
Optimizing Large Language Models for Robust Domain-Specific Text-to-SQL: From Prompting to Preference Alignment
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.contributor.affiliation
ETH Zurich, Switzerland
-
dc.contributor.affiliation
IBM Research - Austin, United States of America (the)
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dc.description.startpage
64
-
dc.description.endpage
75
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dc.type.category
Full-Paper Contribution
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tuw.booktitle
Proceedings of the 11th Edition of the Swiss Text Analytics Conference
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tuw.peerreviewed
true
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tuw.relation.publisher
Association for Computational Linguistics
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tuw.researchTopic.id
I1
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tuw.researchTopic.id
I4
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tuw.researchTopic.name
Logic and Computation
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tuw.researchTopic.name
Information Systems Engineering
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tuw.researchTopic.value
20
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tuw.researchTopic.value
80
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tuw.publication.orgunit
E194-07 - Forschungsbereich Data Management
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dc.description.numberOfPages
12
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tuw.author.orcid
0009-0000-1505-4667
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tuw.event.name
11th Swiss Text Analytics Conference (SwissText 2026)
en
tuw.event.startdate
10-06-2026
-
tuw.event.enddate
10-06-2026
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tuw.event.online
On Site
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tuw.event.type
Event for scientific audience
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tuw.event.place
Zürich
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tuw.event.country
CH
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tuw.event.presenter
Hampp, Noah
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wb.sciencebranch
Informatik
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wb.sciencebranch
Mathematik
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wb.sciencebranch.oefos
1020
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wb.sciencebranch.oefos
1010
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90
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10
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Publications
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en
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http://purl.org/coar/resource_type/c_5794
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conference paper
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item.fulltext
no Fulltext
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item.grantfulltext
none
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crisitem.author.dept
ETH Zurich, Switzerland
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crisitem.author.dept
E192-02 - Forschungsbereich Databases and Artificial Intelligence
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crisitem.author.dept
IBM Research - Austin, United States of America (the)