Can Transformers Learn Sequential Function Classes In Context?
Can Transformers Learn Sequential Function Classes In Context?
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Author / Creator
Campbell, Ryan , Guo, Emma , Hu, Evan , Reya Vir and Hsiao, Ethan
Publisher
Ithaca: Cornell University Library, arXiv.org
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Language
English
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Ithaca: Cornell University Library, arXiv.org
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Contents
In-context learning (ICL) has revolutionized the capabilities of transformer models in NLP. In our project, we extend the understanding of the mechanisms underpinning ICL by exploring whether transformers can learn from sequential, non-textual function class data distributions. We introduce a novel sliding window sequential function class and emplo...
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Full title
Can Transformers Learn Sequential Function Classes In Context?
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TN_cdi_proquest_journals_2904764160
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2904764160
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E-ISSN
2331-8422
