Papers with data changing

1 papers
Dynamic Benchmarking of Masked Language Models on Temporal Concept Drift with Multiple Views (2023.eacl-main)

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Challenge: Temporal concept drift is a problem of data changing over time.
Approach: They benchmark 11 pretrained masked language models on a series of tests to evaluate temporal concept drift.
Outcome: The proposed framework evaluates 11 pretrained masked language models on a series of tests . it aims to reveal how robust an MLM is over time and provide a signal in case it has become outdated .

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