cyclecycle
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https://groundedai.company/
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Comment · 6 days ago
yeah i work on this at https://groundedai.company/
we build a product called Veracity focused on citation and claim checking. legal is a major focus for us, also scientific and technical publishing, where we detect hallucinated citations in submitted works and all that jazz
planning to release something soon focused on the LLM harness layer
we've been working on the problem for years. gradually getting traction
all completely closed source atm, though we often revisit that discussion
i would contest a sibling comment - the solution is not RAG, at least not entirely. 1) because it doesn't necessarily involve 'generation' (e.g our grounding model doesn't depend on gen ai) and 2) RAG is just enriching the context. even if you can get the retrieval right (not a given) it doesn't guarentee that the LLM will faithfully or correctly use that context, or that all its claims are supported by sources. RAG is not the same as traceability and verification
Comment · 23 days ago
Resonates exactly with my experience.
I'll add they also seem to make 'bad' judgement calls around which layer to implement something, often preferring downstream fixes/hacks/workarounds when upstream fixes would be better.
They also duplicate code lots rather than finding and reusing existing.
Both of these patterns lead to degradation and time wasting in complex code bases. Part of the behavioural adaptation I've made in using these tools is heading these off as much as possible at prompt-time
My prompts are often verbose to several paragraphs
Comment · 5 months ago
So far we basically just provide a very rule-based approach and try not use LLMs as much as possible. So we extract and parse the citations using various ML and rule-based approaches, and carry out a bunch of predetermined queries and do various fuzzy matching approaches on the metadata components, and have a bunch of rules around risk levels of things we should have found/matched based on what type of source it is, which venue we should have found it in, etc.
So there are absolutely a bunch of tasks that could be evaled/benchmarked, but "hallucination rate" isn't particularly applicable/interesting as a metric of how good the tool is
that said, we do use various LLMs (mostly local, fine-tuned, small, for things like NER/parsing/metadata comparison, etc.). and they can and do hallucinate, but we have very hard constraints on the validation, so any extraction results that don't match 1:1 back to the input text are discarded for example. so again, rather than hallucination risk we prefer hard constraints
Comment · 5 months ago
This has become such a problem in scholarly publishing that we have a business that provides citation checking https://groundedai.company/ that we've been buidling for a couple of years now
Comment · 7 months ago
Great to see this picked up.
Nick Morley from Grounded AI here (https://groundedai.company)
We ran the analysis in collaboration with Nature :)
Comment · 7 months ago
Nick Morley from Grounded AI here (https://groundedai.company)
We collaborated with Nature here to study the extent of fake/frankenstein citations in scholarly literature (from top 5 publishers - Springer, Elsevier, Wiley, Sage, Taylor & Francis)
We're estimating hundreds of thousands of papers affected in 2025 with hallucinated citation issues
As part of the work we analysed 20k papers generated with ChatGPT API to figure out which citation errors are characteristic of gen AI use and use that classify the errors we saw in the wild.
The world's gone mad, publishing is in a nuts state, the training data is poisoned!
Comment · 7 months ago
yeah this makes sense. we run a citation verification service and provide publishers with data of hey this citation could be fake etc. but we don't currently capture any "action" or "measured result" so i guess that's what we need to expand to next
Comment · 7 months ago
Trying to think about our best chance of making a lasting contribution (therefore actually surviving?) as a startup.
I'm a big fan of focusing on verification tools ("Verification is all you need"?) but who knows.
I'm sure I'm missing something in my perspective. Here to expand my aperture
Comment · 1 year ago
A classic case.
I work on Veracity https://groundedai.company/veracity/ which does citation checking for academic publishers. I see stuff like this all the time in paper submissions. Publishers are inundated
Comment · 2 years ago
That's basically what we're doing with app.studyrecon.ai.
What we've found is that vector similarity is often not the final solution. It is still only a crude proxy for the true goal of 'informativeness' or 'usefulness' with relation to the user goal/query. Works okay, but we're definitely seeing a need for more rigorous LLM-postprocessing to enrich the results set.
Which, yes, the time adds up quick!