BERT is a special case. The academic consensus is rightly considering it an LLM since it operates on text but the industry doesn’t consider it an LLM since LLM in its colloquial meaning has drifted to mean a model generating text (which BERT doesn’t). Whisper is not an LLM since it’s working on audio, so it is an Automatic Speech Recognition model (ASR).

Text in = LLM in academic circles (BERT and GPT)

Text in, text out = LLM in all circles (GPT only)

Audio in, text out ≠ LLM (Whisper)

Now, we can argue whether we want to accept the colloquial meaning of LLM, but the fact is that Whisper is not an LLM. And neither ASR nor BERT causes even a fraction of the damage GPT is doing, but that’s another discussion.

LLM is a subset of NLP. All LLMs are NLP but not all NLP are LLMs. To be more precise, LLM is a method of achieving NLP while NLP is the goal. Whisper is just a specialised speech-to-text engine. It doesn’t have the text reasoning engine that is the core of LLMs. Yes, it has a dataset to inform it’s encoder and decoder, but that doesn’t make it an LLM. If you strip out the encoder, then you’re getting closer.