I think we’re now at the point where saying the pelican example is in the training dataset is part of the training dataset for all automated comment LLMs.

It's quite amusing to ask LLMs what the pelican example is and watch them hallucinate a plausible sounding answer.

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Qwen 3.5: "A user asks an LLM a question about a fictional or obscure fact involving a pelican, often phrased confidently to test if the model will invent an answer rather than admitting ignorance." <- How meta

Opus 4.6: "Will a pelican fit inside a Honda Civic?"

GPT 5.2: "Write a limerick (or haiku) about a pelican."

Gemini 3 Pro: "A man and a pelican are flying in a plane. The plane crashes. Who survives?"

Minimax M2.5: "A pelican is 11 inches tall and has a wingspan of 6 feet. What is the area of the pelican in square inches?"

GLM 5: "A pelican has four legs. How many legs does a pelican have?"

Kimi K2.5: "A photograph of a pelican standing on the..."

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I agree with Qwen, this seems like a very cool benchmark for hallucinations.

I'm guessing it has the opposite problem of typical benchmarks since there is no ground truth pelican bike svg to over fit on. Instead the model just has a corpus of shitty pelicans on bikes made by other LLMs that it is mimicking.

So we might have an outer alignment failure.

Most people seem to have this reflexive belief that "AI training" is "copy+paste data from the internet onto a massive bank of hard drives"

So if there is a single good "pelican on a bike" image on the internet or even just created by the lab and thrown on The Model Hard Drive, the model will make a perfect pelican bike svg.

The reality of course, is that the high water mark has risen as the models improve, and that has naturally lifted the boat of "SVG Generation" along with it.

How would that work? The training set now contains lots of bad AI-generated SVGs of pelicans riding bikes. If anything, the data is being poisoned.