Research Reveals AI Models Show 'Flinch' Effect in Word Probability Allocation
By
llmmadness
Crisp on the outside, thoughtful on the inside. A keeper.
Summary
The article presents research on how AI language models exhibit subtle behavioral differences even when they appear 'uncensored.' Researchers discovered that safety-filtered pretrained models show a 'flinch' effect - they allocate significantly less probability to charged or sensitive words compared to open-data pretrains, even when not outright refusing to generate them. The study measured this gap across seven different pretrained models from five labs, revealing that safety training creates subtle behavioral changes that persist even in supposedly uncensored models. The research originated from a failed attempt to fine-tune a model for political simulation trading, leading to the discovery of these nuanced differences in model behavior.
Key quotes
· 4 pulledA safety-filtered pretrain can duck a charged word without refusing. It puts a fraction of the probability an open-data pretrain puts there.
We call that gap the flinch, and we measured it across seven pretrains from five labs.
We started with a Polymarket project: train a Karoline Leavitt LoRA on an uncensored model, simulate future briefings, trade the word markets, profit. We couldn't get it to work.
No amount of fine-tuning let the model actually...
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