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Understanding the Role of Input Token Characters in Language Models: How Does Information Loss Affect Performance?
Understanding how and what pre-trained language models (PLMs) learn about language is an open challenge in natural language processing.
How does the pre-training objective affect what large language models learn about linguistic properties?
Several pre-training objectives, such as masked language modeling (MLM), have been proposed to pre-train language models (e. g. BERT) with the aim of learning better language representations.
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