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        <title>Semantic Accuracy in Natural Language Generation: A Thesis Proposal</title>
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        <namePart type="given">Patricia</namePart>
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            <title>Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)</title>
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        <name type="personal">
            <namePart type="given">Vishakh</namePart>
            <namePart type="family">Padmakumar</namePart>
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        <name type="personal">
            <namePart type="given">Gisela</namePart>
            <namePart type="family">Vallejo</namePart>
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        <name type="personal">
            <namePart type="given">Yao</namePart>
            <namePart type="family">Fu</namePart>
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    <abstract>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.</abstract>
    <identifier type="citekey">schmidtova-2023-semantic</identifier>
    <identifier type="doi">10.18653/v1/2023.acl-srw.48</identifier>
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%0 Conference Proceedings
%T Semantic Accuracy in Natural Language Generation: A Thesis Proposal
%A Schmidtova, Patricia
%Y Padmakumar, Vishakh
%Y Vallejo, Gisela
%Y Fu, Yao
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F schmidtova-2023-semantic
%X 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.
%R 10.18653/v1/2023.acl-srw.48
%U https://aclanthology.org/2023.acl-srw.48
%U https://doi.org/10.18653/v1/2023.acl-srw.48
%P 352-361

Markdown (Informal)

[Semantic Accuracy in Natural Language Generation: A Thesis Proposal](https://aclanthology.org/2023.acl-srw.48) (Schmidtova, ACL 2023)

• Semantic Accuracy in Natural Language Generation: A Thesis Proposal (Schmidtova, ACL 2023)

ACL

• Patricia Schmidtova. 2023. Semantic Accuracy in Natural Language Generation: A Thesis Proposal. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), pages 352–361, Toronto, Canada. Association for Computational Linguistics.