[0101111101]

Sure! I first used openai embeddings on all the paper titles, abstracts and authors. When a user submits a search query, I embed the query, find the closest matching papers and return those results. Nothing too fancy involved!

I'm also maintaining a dataset of all the embeddings on kaggle if you want to use them yourself: https://www.kaggle.com/datasets/tomtum/openai-arxiv-embeddin...

[heisenburgzero]

So did you just combine Title+Abstracts+Authors into a single chunk and embed them or embedded them individually?

[synctext]

Impressive! Will you parse the papers in the future? Without citations this is not that usable for professors or scientists in general. The relevance ranking largely depends on showing these older, prominent papers. (from our lab experience building decentralised search using transformers)

[0101111101]

One chunk embedded together

[cluckindan]

That method can break when author names and subject matter collide.

[0101111101]

True, but similarly if your embeddings are any good they'll capture interesting associations between authors, topics and your search query. If you find any interesting author overlap results I'd be very interested!

[cluckindan]

Not exactly what I was looking for, but interesting nonetheless: https://arxivxplorer.com/?q=exotic+penis

[elliotec]

Thank you!!