

I see, thanks for clarifying.
I think that concern is partly covered by their scoring. If a bad-faith actor put together a distorted gathering of papers that favored their conclusions but weren’t cited widely, those papers would have very small circles.
So it would be visually apparent that either: they were being dishonest in their research gathering, or the question has not yet been studied widely enough for this tool to be useful.
The more I think about this the more I love this project and their way of displaying the state of consensus on a question.



And this isn’t even really a great application for RAG. Papermaps just goes off of references and citations. Perhaps a sentiment analysis would be marginally useful, but since you need a human to verify all LLM outputs it would be a dubious time savings.
The system scores review papers very favorably and the “yes/no/maybe” conclusion is right in the abstract, usually the last sentence or two of it. This is not a prime candidate for any LLM, it’s simple database operations on srtuctured data that already exists. There’s no use case here.