Leveraging Large Language Models for Building Interpretable Rule-Based Data-to-Text Systems
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F24%3A10492915" target="_blank" >RIV/00216208:11320/24:10492915 - isvavai.cz</a>
Result on the web
<a href="https://aclanthology.org/2024.inlg-main.48/" target="_blank" >https://aclanthology.org/2024.inlg-main.48/</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Leveraging Large Language Models for Building Interpretable Rule-Based Data-to-Text Systems
Original language description
We introduce a simple approach that uses a large language model (LLM) to automatically implement a fully interpretable rule-based data-to-text system in pure Python. Experimental evaluation on the WebNLG dataset showed that such a constructed system produces text of better quality (according to the BLEU and BLEURT metrics) than the same LLM prompted to directly produce outputs, and produces fewer hallucinations than a BART language model fine-tuned on the same data. Furthermore, at runtime, the approach generates text in a fraction of the processing time required by neural approaches, using only a single CPU.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
R - Projekt Ramcoveho programu EK
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 17th International Natural Language Generation Conference
ISBN
979-8-89176-122-3
ISSN
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e-ISSN
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Number of pages
9
Pages from-to
622-630
Publisher name
Association for Computational Linguistics
Place of publication
Kerrville, TX, USA
Event location
Tokyo, Japan
Event date
Sep 23, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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