> For the non technical, this is like asking a delivery driver to drop off packages at 3 houses at the same time without ever stopping the truck.

I'm already hallucinating about how this could work and it involves catapults

Hallucination rate scores are a little tricky to interpret because they're conditional on the model not knowing the answer. That means they don't measure the probability of your encountering a hallucination in everyday use, since that also depends on the probability of the model not knowing the answer, as well as how well your distribution of tasks aligns with the distribution tested in the eval.

I'd also hesitate to attribute this difference in hallucination rates purely to model size. Yes, GLM-5.2 hallucinates much less frequently than DeepSeek-V4 Pro with twice as many parameters, but DeepSeek-V4 Flash is less than half the size of GLM-5.2 and tops the AA-Omniscience hallucination index. Opus 4.8, which is likely larger than DeepSeek-V4 Pro, has a 36% hallucination rate on the index, above GLM-5.2's 28%, but way below the DeepSeek numbers. Opus also has a 47% accuracy rate vs GLM-5.2's 25%. If you use these numbers to calculate the absolute hallucination rate (i.e., the number of hallucinated responses divided by the total number of responses), you get 19% for Opus and 21% for GLM-5.2.

So yes, all else equal larger models may be more prone to hallucination in scenarios where they don't know the answer, but there are a lot of other factors that affect hallucination rates, and it's not totally clear that this is the main metric that's worth tracking.

My anecdotal experience differs (though I hold ground that LLM evaluations are highly subjective and benchmarks are just as useful for LLMs as they are for dating websites users).

GLM 5.2 tends to stray way more than and 5.1. It also hallucinates you things subtly: morphs requirements, makes unfounded conclusions. This output is not something I experienced in any model I seen so far.

In coding it's especially annoying because it steers whole request. E.g. I give instruction: "make we a Rust-WASM-Canvas app" and GLM 5.2 goes like "Oh user surely doesn't mean that. I'll better build Dioxus app instead".

> It’s been proven that when a model is trained on large volumes of highly factual and non-theoretical data, it learns to always have an answer. DeepSeek V4 Pro (1.6T params, 49B active, 44 AA Intelligence Index score) has a ludicrous 94% hallucination score on the AA-Omniscience benchmark, meaning on questions that it couldn’t figure out, it only stated that it didn’t know around 6% of the time, and the rest it confidently hallucinated an answer. GLM-5.2 scored a 28% hallucination rate, Opus 4.8 was 36%, Fable 5 was 48%, and GPT-5.5 was 86%.

Wow! I already knew from previous research shared here that hallucinations are a fundamental problem for LLMs and likely to be unfixable, just like prompt injection, but I didn't realize the hallucination rates were so bad!

Everyone has been acting like the best models only hallucinate in edge cases, but even the best performing one mentioned here - GLM-5.2 - has a hallucination rate of 28% when it doesn't "know" the answer to something.

That said, I think the title on the blog - "Bigger models are not the way" is probably more fitting and touches on what should be even bigger news. If bigger models and bigger training sets have already stopped producing proportional returns, then it seems likely we are already near the top of the S-curve. That's huge news, considering the valuation of companies like OpenAI and xAI is largely based around the (absurd) idea of ever increasing scaling from these models.

> meaning on questions that it couldn’t figure out, it only stated that it didn’t know around 6% of the time, and the rest it confidently hallucinated an answer.

From how they measure it, a model that simply answers "I don't know." to any prompt would be the one hallucinates the least. So it's not surprising at all that a smaller model can perform better.

I think hallucination rates are not a matter of model size but depends on the training of the model. They have been trained on a huge corpus of material that had overwhelmingly well formed questions and we'll formulated and correct answers. This is typically the case of books where the material is highly curated by experts in the field. In a book you never see a question which admit no answer and the book just reasoning and explaining why and how the question has no answer. Neither you will see a good question and the book explaining candidly it doesn't know the answer , because the way the book material is curated the author will omit discussing the question for which it has no answers.

In addition, I think that during HFRL, the labs has a bias for interesting answers that admit a solution and under represent the "bad" questions that admit no good answer. In addition they probably do less effort to HFRL on questions the model should admit it doesn't know.

As humans we have been trained all our lives, in the real world, to be confronted with questions we don't know the response right away and we learned to very quickly assess that we don't know or that we are not sure about the answer.

Another thing we have and LLM have not is fear. We have an amygdala in our brain, separated from the logic thinking part, that can raise a signal of fear so that we get much more carefully about what we say. On the other LLM has no fear organ like the amygdala and just learn to respond based on the patterns in it's training corpus. It never "fears" looking bad or being fired because it gave a wrong answer so it can merrily give perfectly wrong answers.

So, we see hallucination rates can be improved with training but currently the lab are not optimizing for that because there is an high stake race to get the most intelligent and capable model.

Alternatively I can see creating a separate amygdala-like organ for an LLM and that organ may asynchronously fires signal, based on the user prompt and the LLM thinking trace, to inject into the LLM reasoning a fear signal so that it can steer it's answer to something more safe.

The fact that a huge amount uf parameters may lead to worse hallucinations is something I didn't think of. Would this somewhat imply that DeepSeek V4 flash should be less prone tho these issues?

Please don't editorialize titles unless the original title is misleading.

Why is everyone expecting LLMs to be like the Star Trek computer? I wonder if anyone's ever measured what the hallucination rate of a human is.

>GPT-5.5 and DeepSeek V4 Pro are two of the clearest hallucination leaders, despite being absolutely huge. Because of their immense size they simply did not learn how to say “I don’t know” or recognize intricate logical and technical fallacies. While it is true that a multi-trillion parameter model will always beat a lightweight consumer model on paper (today at least), the commoditization of these huge models is blurring the line between benchmark performance and actual real-world truthfulness and accuracy.

What about using two models, with a smaller model used for this kind of negative reasoning?

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This is where I asked GPT 5.5

"they say u hallucinate 3x more than GLM 5.2, whats your comeback to this? do i need to dump u? $article"