oh boy, this looks like an alchemy to me. on one hand one cant deny success of llm's on another we are shifting the responsibiliteis to non-deterministic fuzzy duck-taped functions.
My impression is similar and that's why I asked the question.
Another thing that is not clear to me: Is there query directly fed into e.g. SBERT or should I ask an LLM to transform the query into something more suitable, like turning the question into a proposition?
Asked more abstractly: In a vector space like SBERT's, can I expect questions and answers about the same topic to lie near each other? Especially will the correct answers lie near their question?
>Another thing that is not clear to me: Is there query directly fed into e.g. SBERT or should I ask an LLM to transform the query into something more suitable, like turning the question into a proposition?
This not how 99% of embedding models work(though you can train for specific tasks) but as it turns out such a thing is possible and is beneficial.
This kind of thing is going to seem so obvious in hindsight in six months once all the sota methods start converge upon similar improvements lol. It’s still so early, I keep telling myself.
Another thing that is not clear to me: Is there query directly fed into e.g. SBERT or should I ask an LLM to transform the query into something more suitable, like turning the question into a proposition?
Asked more abstractly: In a vector space like SBERT's, can I expect questions and answers about the same topic to lie near each other? Especially will the correct answers lie near their question?