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        <title>Leveraging Large Language Models for Building Interpretable Rule-Based Data-to-Text Systems</title>
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        <namePart type="given">Jędrzej</namePart>
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            <title>Proceedings of the 17th International Natural Language Generation Conference</title>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Leveraging Large Language Models for Building Interpretable Rule-Based Data-to-Text Systems
%A Warczyński, Jędrzej
%A Lango, Mateusz
%A Dusek, Ondrej
%Y Mahamood, Saad
%Y Minh, Nguyen Le
%Y Ippolito, Daphne
%S Proceedings of the 17th International Natural Language Generation Conference
%D 2024
%8 September
%I Association for Computational Linguistics
%C Tokyo, Japan
%F warczynski-etal-2024-leveraging-large
%X 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.
%U https://aclanthology.org/2024.inlg-main.48
%P 622-630

Markdown (Informal)

[Leveraging Large Language Models for Building Interpretable Rule-Based Data-to-Text Systems](https://aclanthology.org/2024.inlg-main.48) (Warczyński et al., INLG 2024)

• Leveraging Large Language Models for Building Interpretable Rule-Based Data-to-Text Systems (Warczyński et al., INLG 2024)

ACL

• Jędrzej Warczyński, Mateusz Lango, and Ondrej Dusek. 2024. Leveraging Large Language Models for Building Interpretable Rule-Based Data-to-Text Systems. In Proceedings of the 17th International Natural Language Generation Conference, pages 622–630, Tokyo, Japan. Association for Computational Linguistics.