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        <title>Memorisation versus Generalisation in Pre-trained Language Models</title>
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        <name type="personal">
            <namePart type="given">Preslav</namePart>
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    <abstract>State-of-the-art pre-trained language models have been shown to memorise facts and perform well with limited amounts of training data. To gain a better understanding of how these models learn, we study their generalisation and memorisation capabilities in noisy and low-resource scenarios. We find that the training of these models is almost unaffected by label noise and that it is possible to reach near-optimal results even on extremely noisy datasets. However, our experiments also show that they mainly learn from high-frequency patterns and largely fail when tested on low-resource tasks such as few-shot learning and rare entity recognition. To mitigate such limitations, we propose an extension based on prototypical networks that improves performance in low-resource named entity recognition tasks.</abstract>
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%0 Conference Proceedings
%T Memorisation versus Generalisation in Pre-trained Language Models
%A Tänzer, Michael
%A Ruder, Sebastian
%A Rei, Marek
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F tanzer-etal-2022-memorisation
%X State-of-the-art pre-trained language models have been shown to memorise facts and perform well with limited amounts of training data. To gain a better understanding of how these models learn, we study their generalisation and memorisation capabilities in noisy and low-resource scenarios. We find that the training of these models is almost unaffected by label noise and that it is possible to reach near-optimal results even on extremely noisy datasets. However, our experiments also show that they mainly learn from high-frequency patterns and largely fail when tested on low-resource tasks such as few-shot learning and rare entity recognition. To mitigate such limitations, we propose an extension based on prototypical networks that improves performance in low-resource named entity recognition tasks.
%R 10.18653/v1/2022.acl-long.521
%U https://aclanthology.org/2022.acl-long.521/
%U https://doi.org/10.18653/v1/2022.acl-long.521
%P 7564-7578

Markdown (Informal)

[Memorisation versus Generalisation in Pre-trained Language Models](https://aclanthology.org/2022.acl-long.521/) (Tänzer et al., ACL 2022)

• Memorisation versus Generalisation in Pre-trained Language Models (Tänzer et al., ACL 2022)

ACL

• Michael Tänzer, Sebastian Ruder, and Marek Rei. 2022. Memorisation versus Generalisation in Pre-trained Language Models. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7564–7578, Dublin, Ireland. Association for Computational Linguistics.