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Show HN: Gemini Pro 3 imagines the HN front page 10 years from now

(dosaygo-studio.github.io)
3338 points keepamovin | 3 comments | 09 Dec 25 15:00 UTC | HN request time: 0.661s | source
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jll29 ◴[10 Dec 25 12:24 UTC] No.46216933[source]▶
AI professor here. I know this page is a joke, but in the interest of accuracy, a terminological comment: we don't call it a "hallucination" if a model complies exactly with what a prompt asked for and produces a prediction, exactly as requested.

Rater, "hallucinations" are spurious replacements of factual knowledge with fictional material caused by the use of statistical process (the pseudo random number generator used with the "temperature" parameter of neural transformers): token prediction without meaning representation.

[typo fixed]

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articlepan ◴[10 Dec 25 12:56 UTC] No.46217166[source]▶
I agree with your first paragraph, but not your second. Models can still hallucinate when temperature is set to zero (aka when we always choose the highest probability token from the model's output token distribution).

In my mind, hallucination is when some aspect of the model's response should be consistent with reality but is not, and the reality-inconsistent information is not directly attributable or deducible from (mis)information in the pre-training corpus.

While hallucination can be triggered by setting the temperature high, it can also be the result of many possible deficiencies in model pre- and post- training that result in the model outputting bad token probability distributions.

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ActivePattern ◴[10 Dec 25 13:46 UTC] No.46217642[source]▶
I've never heard the caveat that it can't be attributable to misinformation in the pre-training corpus. For frontier models, we don't even have access to the enormous training corpus, so we would have no way of verifying whether or not it is regurgitating some misinformation that it had seen there or whether it is inventing something out of whole cloth.
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Aurornis ◴[10 Dec 25 13:54 UTC] No.46217736[source]▶
> I've never heard the caveat that it can't be attributable to misinformation in the pre-training corpus.

If the LLM is accurately reflecting the training corpus, it wouldn’t be considered a hallucination. The LLM is operating as designed.

Matters of access to the training corpus are a separate issue.

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1. CGMthrowaway ◴[10 Dec 25 14:53 UTC] No.46218354[source]▶
> If the LLM is accurately reflecting the training corpus, it wouldn’t be considered a hallucination. The LLM is operating as designed.

That would mean that there is never any hallucination.

The point of original comment was distinguishing between fact and fiction, which an LLM just cannot do. (It's an unsolved problem among humans, which spills into the training data)

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2. Aurornis ◴[10 Dec 25 15:11 UTC] No.46218549[source]▶
> That would mean that there is never any hallucination.

No it wouldn’t. If the LLM produces an output that does not match the training data or claims things that are not in the training data due to pseudorandom statistical processes then that’s a hallucination. If it accurately represents the training data or context content, it’s not a hallucination.

Similarly, if you request that an LLM tells you something false and the information it provided is false, that’s not a hallucination.

> The point of original comment was distinguishing between fact and fiction,

In the context of LLMs, fact means something represented in the training set. Not factual in an absolute, philosophical sense.

If you put a lot of categorically false information into the training corpus and train an LLM on it, those pieces of information are “factual” in the context of the LLM output.

The key part of the parent comment:

> caused by the use of statistical process (the pseudo random number generator

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3. CGMthrowaway ◴[10 Dec 25 15:45 UTC] No.46219042[source]▶
OK if everyone else agrees with your semantics then I agree