For every application of semantic search, I’d love to see what the benefit is over text search.
If there a benchmark to see if it improves the search. Subjectively, did you find it surfaced new papers? Is this more useful in certain domains?
All benefits depend on the ability of the embedding model. Semantic embeddings understand nuances, so they can match abstracts that align conceptually even if no exact keywords overlap. For example, "neural networks" vs. "deep learning." can and should fetch similar papers.
Subjectively, yes. I sent this around my peers and they said it helped them find new authors/papers in the field while preparing their manuscripts.
| Is this more useful in certain domains?
I don't think I have the capacity to comment on this.
One of the factors is how users phrase their queries. On some level people are used to full text search but semantic shines when they ask literal questions with terminology that may not match the answer.
Exactly. Full text paradigm has it's own pros and I believe we need those tools in the new vector search to take full advantage. I am planning to add keywords feature where if a user enters something in "quotes", the would need to be in the shown results. Just like you can do with a google search.
