Can ChatGPT Truly Understand What We’re Saying? A Powerful Comparison with BERT” – Day 69

  Transformer Models Comparison Feature BERT GPT BART DeepSeek Full Transformer Uses Encoder? ✅ Yes ❌ No ✅ Yes ❌ No ✅ Yes Uses Decoder? ❌ No ✅ Yes ✅ Yes ✅ Yes ✅ Yes Training Objective Masked Language Modeling (MLM) Autoregressive (Predict Next Word) Denoising Autoencoding Mixture-of-Experts (MoE) with Multi-head Latent Attention (MLA) Sequence-to-Sequence (Seq2Seq) Bidirectional? ✅ Yes ❌ No ✅ Yes (Encoder) ❌ No Can be both Application NLP tasks (classification, Q&A, search) Text generation (chatbots, summarization) Text generation and comprehension (summarization, translation) Advanced reasoning tasks (mathematics, coding) Machine translation, speech-to-text   Understanding ChatGPT and BERT: A Comprehensive Analysis by Zhong et al. (2023). The advancements in natural language processing (NLP) have been greatly influenced by transformer-based models like ChatGPT and BERT. Although both are built on the transformer architecture, they serve different purposes and exhibit unique strengths. This blog post explores the mathematical foundations, architectural differences, and performance capabilities of these two models, integrating insights from the recent comparative study by Zhong et al. (2023). The Transformer Architecture Click here to view the Transformer Architecture on Jalammar’s website (Illustrated Transformer) At the core of both ChatGPT and BERT is the transformer architecture, which revolutionized how models process…