[happy_dog1]

I wonder whether for a lot of the search & literature review-type use-cases where people are trying to use GPT-5 and similar we'd honestly be much better off with a really powerful semantic search engine? Any time you ask a chatbot to summarize the literature for you or answer your question, there's a risk it will hallucinate and give you an unreliable answer. Using LLM-generated embeddings for documents to retrieve the nearest match, by contrast, doesn't run any risk of hallucination and might be a powerful way to retrieve things that Google / Bing etc. wouldn't be able to find using their current algorithms.

I don't know if something like this already exists and I'm just not aware of it to be fair.

[Timsky]

I think you have a very good point here: a semantic search would be the best option for such a search. The items would have unique identifiers so the language variations can be avoided. But unfortunately, I am not aware of any of these kinds of publicly available projects, except DBpedia and some biology-oriented ontologies that would massively analyze scientific reports.

Currently, I am applying RDF/OWL to describe some factual information and contradictions in the scientific literature. On an amateur level. Thus I do it mostly manually. The GPT-discourse somehow brings up not only the human-related perception problems, such as cognitive biases, but also truly philosophical questions of epistemology that should be resolved beforehand. LLM developers cannot solve this because it is not under their control. They can only choose what to learn from. For instance, when we consider a scientific text, it is not an absolute truth but rather a carefully verified and reviewed opinion that is based on the previous authorized opinions and subject to change in the future. So the same author may have various opinions over time. More recent opinions are not necessarily more "truthful" ones. Now imagine a corresponding RDF triple (subject-predicate-object tuple) that describes that. Pretty heavy thing, and no NLTK can decide for us what the truth is and what is not.

[opdahl]

Since you specifically were wondering if something like this exist, I feel okay with mentioning my own tool https://keenious.com since I think it might fit your needs.

Basically we are trying to combine the benefits of chat with normal academic search results using semantic search and keyword search. That way you get the benefit of LLMs but you’re actually engaging with sources like a normal search.

Hope it was what you were looking for!

[wasabi991011]

Not who you were responding to, but your tool seems interesting, I'll check it out!

Can I ask, how did you build your search database?

[astrange]

If your issue is "incorrect answers" (hallucination) then an embedding search will naturally also produce "incorrect answers" because an embedding is a lossy compression of the source.