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        <title>A Comprehensive Analysis of Memorization in Large Language Models</title>
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        <namePart type="given">Hirokazu</namePart>
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        <namePart type="given">Issa</namePart>
        <namePart type="family">Sugiura</namePart>
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    <name type="personal">
        <namePart type="given">Daisuke</namePart>
        <namePart type="family">Kawahara</namePart>
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    <name type="personal">
        <namePart type="given">Sadao</namePart>
        <namePart type="family">Kurohashi</namePart>
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            <title>Proceedings of the 17th International Natural Language Generation Conference</title>
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        <name type="personal">
            <namePart type="given">Nguyen</namePart>
            <namePart type="given">Le</namePart>
            <namePart type="family">Minh</namePart>
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    <abstract>This paper presents a comprehensive study that investigates memorization in large language models (LLMs) from multiple perspectives. Experiments are conducted with the Pythia and LLM-jp model suites, both of which offer LLMs with over 10B parameters and full access to their pre-training corpora. Our findings include: (1) memorization is more likely to occur with larger model sizes, longer prompt lengths, and frequent texts, which aligns with findings in previous studies; (2) memorization is less likely to occur for texts not trained during the latter stages of training, even if they frequently appear in the training corpus; (3) the standard methodology for judging memorization can yield false positives, and texts that are infrequent yet flagged as memorized typically result from causes other than true memorization.</abstract>
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%0 Conference Proceedings
%T A Comprehensive Analysis of Memorization in Large Language Models
%A Kiyomaru, Hirokazu
%A Sugiura, Issa
%A Kawahara, Daisuke
%A Kurohashi, Sadao
%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 kiyomaru-etal-2024-comprehensive-analysis
%X This paper presents a comprehensive study that investigates memorization in large language models (LLMs) from multiple perspectives. Experiments are conducted with the Pythia and LLM-jp model suites, both of which offer LLMs with over 10B parameters and full access to their pre-training corpora. Our findings include: (1) memorization is more likely to occur with larger model sizes, longer prompt lengths, and frequent texts, which aligns with findings in previous studies; (2) memorization is less likely to occur for texts not trained during the latter stages of training, even if they frequently appear in the training corpus; (3) the standard methodology for judging memorization can yield false positives, and texts that are infrequent yet flagged as memorized typically result from causes other than true memorization.
%U https://aclanthology.org/2024.inlg-main.45
%P 584-596

Markdown (Informal)

[A Comprehensive Analysis of Memorization in Large Language Models](https://aclanthology.org/2024.inlg-main.45) (Kiyomaru et al., INLG 2024)

• A Comprehensive Analysis of Memorization in Large Language Models (Kiyomaru et al., INLG 2024)

ACL

• Hirokazu Kiyomaru, Issa Sugiura, Daisuke Kawahara, and Sadao Kurohashi. 2024. A Comprehensive Analysis of Memorization in Large Language Models. In Proceedings of the 17th International Natural Language Generation Conference, pages 584–596, Tokyo, Japan. Association for Computational Linguistics.