I’ve seen that 8 out of 10 ML interviews this yr ask about this subject: 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 sturdy context understanding, like studying comprehension, textual content classification, and question-answering programs.
GPT: Developed by OpenAI, GPT is a unidirectional era mannequin targeted on producing pure language content material. Its pre-training purpose is CLM (Causal Language Modeling). GPT excels at duties like article writing, dialog, and code era.
LLAMA: LLAMA, developed by Meta, is a sequence of environment friendly massive language fashions that enhance the present 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 sturdy efficiency. Like GPT, LLAMA’s pre-training purpose can also be CLM (Causal Language Modeling).
In comparison with GPT fashions, LLAMA can obtain comparable and even higher efficiency with fewer assets and smaller information. For instance, LLAMA-7B (7 billion parameters) can compete with GPT-3–175B (175 billion parameters) on many duties. A part of it is because LLAMA is open-source, so it advantages from contributions from a big neighborhood of innovators.
