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If you want to disabuse yourself of your notions annd intuitions on how LLMs work, run safety models and tests.

On a hate speech policy test for a lightweight LLM, the presence or absence of the last full stop on the last sentence would cause the model to flip its decisions.

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I'm not sure how crappy small models behaving unreliably is relevant here, when a large SOTA model does presumably not produce the same issue.

Its relevant because those same issues occur with large models. Also, the "crappy small model", was a model trained for safety tasks, and outperformed the frontier lab safety models.

And you tested this, that the presence of the last full stop flips the outcome with a large SOTA model?

Small models are notoriously unreliable and prone to hallucination in my experience, so that would not surprise me to be an issue there.

I blame the $slur

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