Google introduced BART (Bidirectional Encoder Representations from Transformers) in January 2021, a dialogue generation model based on the BERT architecture. Like ChatGPT, BART is a transformer-based model designed to produce human-like responses by analyzing input context. However, several key distinctions set them apart.
Firstly, while ChatGPT was trained on internet-scraped text, BART leverages a diverse array of sources such as Wikipedia, BooksCorpus, and CommonCrawl. This varied training data enhances BART's ability to generate coherent and contextually relevant responses.
Secondly, BART extends beyond dialogue generation to tasks like summarization, translation, and image captioning. This broader training enables BART to be more versatile compared to ChatGPT.
Furthermore, BART offers users more specific control over generated responses. For instance, users can input a news article summary, prompting BART to generate a relevant summary in response.
The transformer architecture underpinning BART was initially introduced in 2017, allowing it to process sequential data like natural language efficiently. BART itself is a variant of the GPT model by OpenAI, tailored for diverse tasks beyond text generation.
Google AI Language utilized pre-training to train BART on extensive text data, enhancing its understanding of context and structure. Additionally, denoising autoencoding was employed to refine BART's ability to filter out irrelevant information from text data.
Overall, BART represents a significant advancement in natural language processing, offering high-quality, contextually relevant responses across various tasks. While both ChatGPT and BART excel in generating human-like responses, the choice between them hinges on specific application needs and desired response control.