Technology

Just for context, this is the error rate when the right answer is provided to the LLM in a document. This means that even when the answer is being handed to the LLM they fail at the rates provided in the article/paper.

Most people interacting with LLMs aren't asking questions against documents, or the answer can not be directly inferred from the documents (asking the LLM to think about the materials in the documents).

That means in most situations the error rate for the average user will be significantly higher.

How much do large language models actually hallucinate when answering questions grounded in provided documents?

Okay, this is looking promising, at least in terms of the most important qualifications being plainly stated in the opening line.

Because the amount of hallucinations/inaccuracies “in the wild” - depending on the model being tested - runs about 60-80%. But then again, this would be average use on generalized data sets, not questions focusing on specific documentation. So of course the “in the wild” questions will see a higher rate.

This also helps users, as it shows that hallucinations/inaccuracies can be reduced by as much as ⅔ by simply limiting LLMs to specific documentation that the user is certain contains the desired information, rather than letting them trawl world+dog.

Very interesting!

We need to stop calling it hallucinations and say what it is, ERRORS

Firstly, thanks for this paper. I read it this afternoon.

Secondly, well, shit. I'm beavering away at a paper in what little spare time I have, looking at hallucination suppression in local LLM. I've been testing both the abliterated and base version of Qwen3-4B 2507 instruct, as they represent an excellent edge device llm per all benchmarks (also, because I am a GPU peasant and only have 4GB vram). I've come at it from a different angle but in the testing I've done (3500 runs; plus another 210 runs on a separate clinical test battery), it seems that model family + ctx size dominate hallucination risk. Yes, a real "science discovers water makes things wet; news at 11" moment.

Eg: Qwen3-4B Hivemind ablation shows strong hallucination suppression (1.4% → 0.2% over 1000 runs) when context grounded. But it comes with a measured tradeoff: contradiction handling suffers under the constraints (detection metrics 2.00 → 0.00). When I ported the same routing policy to base Qwen 3-4B 2507 instruct, the gains flipped. No improvement, and format retries spiked to 24.9%. Still validating these numbers across conditions; still trying to figure out the why.

For context, I tested:

Reversal: Does the model change its mind when you flip the facts around? Or does it just stick with what it said the first time?

Theory of Mind (ToM): Can it keep straight who knows what? Like, "Alice doesn't know this fact, but Bob does" - does it collapse those into one blended answer or keep them separate?

Evidence: Does it tag claims correctly (verified from the docs, supported by inference, just asserted)? And does it avoid upgrading vague stuff into false confidence?

Retraction: When you give it new information that invalidates an earlier answer, does it actually incorporate that or just keep repeating the old thing?

Contradiction: When sources disagree, does it notice? Can it pick which source to trust? And does it admit uncertainty instead of just picking one and running with it?

Negative Control: When there's not enough information to answer, does it actually refuse instead of making shit up?

Using this as the source doc -

https://tinyurl.com/GuardianMuskArticle

FWIW, all the raw data, scores, and reports are here: https://codeberg.org/BobbyLLM/llama-conductor/src/branch/main/prepub

The Arxiv paper confirms what I'm seeing in the weeds: grounding and fabrication resistance are decoupled. You can be good at finding facts and still make shit up about facts that don't exist. And Jesus, the gap between best and worst model at 32K is 70 percentage points? Temperature tuning? Maybe 2-3 pp gain. I know which lever I would be pulling (hint: pick a good LLM!),

For clinical deployment under human review (which is my interest), I can make the case that trading contradiction flexibility for refusal safety is ok - it assumes the human in the middle reads the output and catches the edge cases.

But if you're expecting one policy to work across all models, automagically, you're gonna have a bad time.

TL;DR: once you control for model family, I think context length is going to turn out the be the main degradation driver; my gut feeling based on the raw data here is that the useful window for local 4B is tighter ~16K. Above that hallucination starts to creep in, grounding or not. It would be neat if it was a simple 4x relationship (4b-->16K; 8b-->32K) but things tend not to work out that nicely IRL.

PS: I think (no evidence yet) that ablit and non abilt might need different grounding policies for different classes of questions. That's interesting too - it might mean we can route between deterministic grounding and not differently, depending on ablation, to get the absolute best hallucination suppression. I need to think more on it.

PPS: I figured out what caused the 24.9% retry spike - my stupid fat fingers when coding. I amended the code and it's now sitting at 0%. What's more, early trends are showing 0.00% hallucinations across testing (I'm about 700 repeats in). I'm going to run a smaller re-test battery (1400 or so) across both Qwen3-4B 2507 models to achieve minimal statistical valid difference. If THAT holds, I will then test on Granite Micro 3B, Phi-4B-mini and Small-llm 3B tomorrow. I think that will give me approx 8000 data points.

If this shows what I hope it shows, then maybe, just maybe ..... no, let's not jinx it. I'll put the data out there and someone else can run confirmation.

My biggest takeaway here is that choosing the context length and (to a lesser extent) the temperature carefully is important for reducing hallucinations. I expected model families to vary widely between themselves but not for context length to have such a massive impact tbh.

It seems from this like reducing context length in applications where it isn’t essential for the model to hold very large amounts of context simultaneously would be best practice no?

This is pretty bonkers. How TF are they fabricating answers?????

This is why I would encourage people to use llms for something not important. like video games or interests. You likely will have enough knowledge around the things to catch the "hallucinations" and hopefully that will give you perspective on their use for more important things.

I'm not good at math, so someone please help me.

If a model hallucinates 1% of the time for every question in a chat window that has 100 prompts in it, what is the chance of receiving a hallucination at some point in the chat?

People call it hallucinating but it seems pretty much identical to rationalization.

At 32K, the best model (GLM 4.5) fabricates 1.19% of answers

Not bad, I don't know many people who are 98.81% accurate in their statements.

GLM 4.5 is from August. Isn’t the real tl;dr that a seven month old open model, which was behind proprietary models at the time, did better than most humans would?