I’ve observed that 8 out of 10 ML interviews this 12 months ask about this matter: the variations between the BERT, GPT, and LLAMA mannequin architectures. Each hiring supervisor appears to deliver it up! Let’s go over it collectively, and be happy to leap in with any corrections or ideas. 😊
BERT: Developed by Google, BERT is a bidirectional textual content understanding mannequin that performs rather well on pure language understanding duties. It makes use of a Transformer encoder, which means it considers each the left and proper context when processing textual content, giving it a full understanding of the context. The pre-training duties are MLM (Masked Language Mannequin) and NSP (Subsequent Sentence Prediction). BERT is nice for duties that want robust context understanding, like studying comprehension, textual content classification, and question-answering methods.
GPT: Developed by OpenAI, GPT is a unidirectional technology mannequin centered on producing pure language content material. Its pre-training aim is CLM (Causal Language Modeling). GPT excels at duties like article writing, dialog, and code technology.
LLAMA: LLAMA, developed by Meta, is a collection of environment friendly giant language fashions that enhance the prevailing Transformer structure for higher effectivity and efficiency. It’s recognized for being environment friendly, making it nice for multi-tasking and dealing with restricted assets whereas nonetheless delivering robust efficiency. Like GPT, LLAMA’s pre-training aim can be CLM (Causal Language Modeling).
In comparison with GPT fashions, LLAMA can obtain comparable and even higher efficiency with fewer assets and smaller knowledge. For instance, LLAMA-7B (7 billion parameters) can compete with GPT-3–175B (175 billion parameters) on many duties. A part of it’s because LLAMA is open-source, so it advantages from contributions from a big group of innovators.
