ALBERT: A Lite BERT for Self-Supervised Learning of Language Representations This paper introduces ALBERT, a lightweight version of BERT designed to address the challenges of large-scale pretraining in natural language processing. ALBERT incorporates two key parameter reduction techniques: factorized embedding parameterization and cross-layer parameter sharing, which significantly reduce the number of parameters while maintaining or improving performance. These techniques enable ALBERT to scale up to larger models with fewer parameters compared to BERT, achieving state-of-the-art results on benchmarks such as GLUE, RACE, and SQuAD. Additionally, ALBERT introduces a self-supervised loss focused on inter-sentence coherence, which consistently improves performance on multi-sentence tasks. The paper provides detailed experimental results and comparisons with BERT, demonstrating the effectiveness of ALBERT's design choices.ALBERT: A Lite BERT for Self-Supervised Learning of Language Representations This paper introduces ALBERT, a lightweight version of BERT designed to address the challenges of large-scale pretraining in natural language processing. ALBERT incorporates two key parameter reduction techniques: factorized embedding parameterization and cross-layer parameter sharing, which significantly reduce the number of parameters while maintaining or improving performance. These techniques enable ALBERT to scale up to larger models with fewer parameters compared to BERT, achieving state-of-the-art results on benchmarks such as GLUE, RACE, and SQuAD. Additionally, ALBERT introduces a self-supervised loss focused on inter-sentence coherence, which consistently improves performance on multi-sentence tasks. The paper provides detailed experimental results and comparisons with BERT, demonstrating the effectiveness of ALBERT's design choices.