For my task, I need a model that can distinguish between job titles that contain the same words. BERT model "msmarco-MiniLM-L-12-v3" shows high cosine similarity for positions: "Data customer" and "Data provider". The meaning of these two positions are very different and I need my model to show a low cosine similarity for these two positions.
However, in this case cosine similarity must be high: "Data customer" "Data consumer".
Which model should I use? Should I train classifier instead of nlu model? Why ChatGPT understands the difference between those texts, but BERT based models show high cosine similarity?