I’ve used bert-large-uncased to generate similarity matrix and cosine similarity to produce internal link recommendations for 5000 pages that don’t already link to each other (source → target). My next step is to select existing word or a short phrase on “source” page to serve as anchor text for “target” page.
I’ve tried both gpt-4 and text-bison (PaLM2) with various parameter changes including temperature, top k, top p, reduced output tokens… and no matter what I do the anchor text recommendation is often made up. I tried simulating the API call in both PaLM2 playground and used chat GPT-4 to reason with and it would confuse them as well.
I then attempted to use bert-ner (failed) and then some basic methods like n-gram analysis, TF-IDF…etc but recommendations, while often keyword-level accurate made no sense in the context of the whole text of both pages.
Am I blind to an obvious solution here?