Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, andLessons Learned

Inside Red Teaming AI: How Teams Find and Fix Harm

We tested language models to see what bad answers they might give and how to make them safer.
Our team ran attacks across three model sizes and four ways the models were set up, and we found some clear patterns.
Models trained with RLHF becomes harder to trick as they grew, while other kinds of models stay about the same as scale changes.
We collected nearly 39,000 attempts to make the models say harmful things and shared that big dataset so others could study it, many surprising examples showed up.
The results include rude language and more subtle unfair or risky replies, so it's not only one issue its many small harms added together.
Red teaming felt messy and uncertain, we explain how we did it and what unsure points remain.
This work is meant to help build better tools and shared rules for everyone, with more people testing models we get safer systems faster.
An open effort need clear tests and honest sharing of failures and wins.
red team harmful outputs

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Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, andLessons Learned

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