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VISLA Benchmark: Evaluating Embedding Sensitivity to Semantic and Lexical Alterations
This paper introduces the VISLA (Variance and Invariance to Semantic and Lexical Alterations) benchmark, designed to evaluate the semantic and lexical understanding of language models.
Breaking the Token Barrier: Chunking and Convolution for Efficient Long Text Classification with BERT
Various complex methods have claimed to overcome this limit, but recent research questions the efficacy of these models across different classification tasks.
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