Comment by bityard
11 hours ago
That is not how models work.
Unless specifically told in a system prompt, the pile of weights has absolutely no knowledge of itself. You could hypothetically train it to answer such questions, but nobody bothers to do this, and ALL "knowledge" embedded in the weights is probabalistic anyway.
(I feel like this should be common knowledge in LLM discussions on HN by now.)
This is less true for modern posttrained models. Model identity can be explicitly reinforced during posttraining. Qwen's own finetuning docs include identity training examples, and Qwen models have been trained with system prompts that explicitly say things like "You are Qwen, created by Alibaba Cloud."
So a model correctly identifying its family doesn't necessarily mean it inferred that from pretraining.
I think with Jev, they took a posttrained model and trained it further, so it did not forget about its earlier knowledge during Owen's own RL.
Right, if a model says it is Qwen there is no way to distinguish a ModernBert fine tuned with Qwen completion data from a Qwen model fine tuned with completion data.
It’s also entirely possible that they used completions from a pool of open weight models.
"<Q>What model are you?<A>Qwen." is surely in Qwen's training data. It's quite standard to include such meta knowledge during instruction tuning.
Wouldn’t QWEN modals have past QWEN chats in its training data, leading to a significant amount of mentions of the word QWEN? Just the question “what model are you” would have been answered deterministically multiple times and they’re now part of the weights.
This is, in fact, not true.
Okay, I stand corrected that _some_ models may include self-identifying metadata in their training. Although I believe I accounted for that in my comment.
Historically, many do not and there are lots of counter-examples proving this. They merely hallucinate an answer just like anything else. The SAME model may even give different answers to the same prompt when asked multiple times... sometimes they claim to be ChatGPT, sometimes Gemma, etc. The fact that the answer is delivered confidently fools people who don't understand this, and these people then run straight to social media with "proof" of their conspiracy theory that one AI lab "stole" another AI lab's model.
My point stands that unless specifically trained or told, big bags of weights do not possess any inherent introspection. LLMs have many fascinating emergent properties, but this is not one of them.
> The SAME model may even give different answers to the same prompt when asked multiple times
temperature?