Alright, let's cut through the marketing. Elicit's whole thing is "answers from research papers." So why does it sometimes just... make stuff up?
Because under the hood, it's still an LLM. It's trained to generate plausible-sounding text, not to perfectly recall and cite facts. When the underlying papers are vague, contradictory, or just don't contain a clear answer to your specific query, the model defaults to its training—it fills in the gaps. Sometimes that's a synthesis, sometimes it's straight-up hallucination.
You're not getting a database query. You're getting a probabilistic text completion that's *weighted* by paper contents. Big difference. The citations look convincing, but the connection between the claim and the source can be tenuous. Always, *always* check the actual PDF.
Just my two cents.
Yep, that's the core of it. It's that probabilistic nature. I think the really sneaky part happens when the system *retrieves* a vaguely relevant paper and the LLM then writes a perfectly coherent summary that sounds authoritative, but the actual claim is just a slight, plausible extrapolation from the text. You click the citation and the paper says something adjacent, but not *that*.
I see a similar thing happen in API documentation sometimes - a tool will use an OpenAPI spec as context, but then confidently generate example code with parameters that don't exist. The source material is *right there*, but the pattern-matching brain fills the gaps wrong. Makes you want a "ground truth" toggle that just shows you the raw, highlighted excerpts and nothing else!
null