BART is a denoising autoencoder for pre-training sequence-to-sequence models, designed for natural language generation, translation, and comprehension. It uses a standard Transformer-based architecture, combining bidirectional encoding with a left-to-right decoder, and is trained by corrupting text with arbitrary noising functions and learning to reconstruct the original text. BART achieves strong performance on various tasks, including text generation, comprehension, and translation. It matches RoBERTa's performance on GLUE and SQuAD, and achieves state-of-the-art results on abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE. BART also improves machine translation performance by 1.1 BLEU over a back-translation system with only target language pre-training. The model is effective for both generation and comprehension tasks, and its performance is consistent across a wide range of tasks. BART's architecture allows for flexible noising schemes, including text infilling and sentence permutation, which enhance its ability to generalize across different tasks. The model is pre-trained on a large scale, with 12 layers in the encoder and decoder and a hidden size of 1024. BART outperforms previous models on several tasks, including SQuAD, GLUE, and summarization, and is particularly effective for abstractive tasks. The model's performance is evaluated on various benchmarks, and it shows strong results in both discriminative and generation tasks. BART's architecture allows for fine-tuning for specific tasks, and it is effective for machine translation when used as a pre-trained decoder. The model's performance is consistent across different tasks, and it demonstrates strong capabilities in natural language understanding and generation.BART is a denoising autoencoder for pre-training sequence-to-sequence models, designed for natural language generation, translation, and comprehension. It uses a standard Transformer-based architecture, combining bidirectional encoding with a left-to-right decoder, and is trained by corrupting text with arbitrary noising functions and learning to reconstruct the original text. BART achieves strong performance on various tasks, including text generation, comprehension, and translation. It matches RoBERTa's performance on GLUE and SQuAD, and achieves state-of-the-art results on abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE. BART also improves machine translation performance by 1.1 BLEU over a back-translation system with only target language pre-training. The model is effective for both generation and comprehension tasks, and its performance is consistent across a wide range of tasks. BART's architecture allows for flexible noising schemes, including text infilling and sentence permutation, which enhance its ability to generalize across different tasks. The model is pre-trained on a large scale, with 12 layers in the encoder and decoder and a hidden size of 1024. BART outperforms previous models on several tasks, including SQuAD, GLUE, and summarization, and is particularly effective for abstractive tasks. The model's performance is evaluated on various benchmarks, and it shows strong results in both discriminative and generation tasks. BART's architecture allows for fine-tuning for specific tasks, and it is effective for machine translation when used as a pre-trained decoder. The model's performance is consistent across different tasks, and it demonstrates strong capabilities in natural language understanding and generation.