Semantic Accuracy in Natural Language Generation: A Thesis Proposal
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A10475849" target="_blank" >RIV/00216208:11320/23:10475849 - isvavai.cz</a>
Result on the web
<a href="https://aclanthology.org/2023.acl-srw.48/" target="_blank" >https://aclanthology.org/2023.acl-srw.48/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.18653/v1/2023.acl-srw.48" target="_blank" >10.18653/v1/2023.acl-srw.48</a>
Alternative languages
Result language
angličtina
Original language name
Semantic Accuracy in Natural Language Generation: A Thesis Proposal
Original language description
With the fast-growing popularity of current large pre-trained language models (LLMs), it is necessary to dedicate efforts to making them more reliable. In this thesis proposal, we aim to improve the reliability of natural language generation systems (NLG) by researching the semantic accuracy of their outputs. We look at this problem from the outside (evaluation) and from the inside (interpretability). We propose a novel method for evaluating semantic accuracy and discuss the importance of working towards a unified and objective benchmark for NLG metrics. We also review interpretability approaches which could help us pinpoint the sources of inaccuracies within the models and explore potential mitigation strategies.
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
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
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 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
ISBN
978-1-959429-69-2
ISSN
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e-ISSN
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Number of pages
10
Pages from-to
352-361
Publisher name
Association for Computational Linguistics
Place of publication
Stroudsburg, PA, USA
Event location
Toronto, Canada
Event date
Jul 9, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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