[wenc]

Can I ask where BERT models are used in production these days?

I was given to understand that they are a better alternative to LLM type models for specific tasks like topic classification because they are trained to discriminate rather than to generate (plus they are bidirectional so they can “understand” context better through lookahead). But LLMs are pretty strong so I wonder if the difference is negligible?

[vietvu]

LLMs like GPT are heavy and costly (and BERT are LLMs too, params can up to like 1.5B). For niche problems like classification on a small domain, BERT like models are much better, cheaper. You don't need all knowledge gen AI LLM has. I have seen many companies using DeBERTa or RoBERTa for text classification, not using GPT/LLaMA.

[ganeshkrishnan]

LLMs dont have the same usecase as encoder only models. Lets assume you have around million keywords and you want to find the most similar to a keyword that the user input.

In pre-processing you would have calculated the vector encoding of all the million keywords before hand and now with the keyword the user input, you calculate the vector and then find the most similar vectors

LLM is used by end user, encoders are used by devs in app to search/retrieve text.