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        <title>PreCog: Exploring the Relation between Memorization and Performance in Pre-trained Language Models</title>
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    <name type="personal">
        <namePart type="given">Leonardo</namePart>
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    <name type="personal">
        <namePart type="given">Elena</namePart>
        <namePart type="given">Sofia</namePart>
        <namePart type="family">Ruzzetti</namePart>
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        <namePart type="given">Fabio</namePart>
        <namePart type="given">Massimo</namePart>
        <namePart type="family">Zanzotto</namePart>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>Large Language Models (LLMs) are impressive machines with the ability to memorize, possibly generalized learning examples. We present here a small, focused contribution to the analysis of the interplay between memorization and performance of BERT in downstream tasks. We propose PreCog, a measure for evaluating memorization from pre-training, and we analyze its correlation with the BERT’s performance. Our experiments show that highly memorized examples are better classified, suggesting memorization is an essential key to success for BERT.</abstract>
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        <url>https://aclanthology.org/2023.ranlp-1.103</url>
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        <date>2023-09</date>
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            <start>961</start>
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%0 Conference Proceedings
%T PreCog: Exploring the Relation between Memorization and Performance in Pre-trained Language Models
%A Ranaldi, Leonardo
%A Ruzzetti, Elena Sofia
%A Zanzotto, Fabio Massimo
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F ranaldi-etal-2023-precog
%X Large Language Models (LLMs) are impressive machines with the ability to memorize, possibly generalized learning examples. We present here a small, focused contribution to the analysis of the interplay between memorization and performance of BERT in downstream tasks. We propose PreCog, a measure for evaluating memorization from pre-training, and we analyze its correlation with the BERT’s performance. Our experiments show that highly memorized examples are better classified, suggesting memorization is an essential key to success for BERT.
%U https://aclanthology.org/2023.ranlp-1.103
%P 961-967

Markdown (Informal)

[PreCog: Exploring the Relation between Memorization and Performance in Pre-trained Language Models](https://aclanthology.org/2023.ranlp-1.103) (Ranaldi et al., RANLP 2023)

• PreCog: Exploring the Relation between Memorization and Performance in Pre-trained Language Models (Ranaldi et al., RANLP 2023)

ACL

• Leonardo Ranaldi, Elena Sofia Ruzzetti, and Fabio Massimo Zanzotto. 2023. PreCog: Exploring the Relation between Memorization and Performance in Pre-trained Language Models. In Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, pages 961–967, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.