This post is organized in three sections, each describing a different pre-training mitigation:

Since training data shapes the capabilities of any learned model, data filtering is a powerful tool for limiting undesirable model capabilities. We applied this approach to two categories—images depicting graphic violence and sexual content—by using classifiers to filter images in these categories out of the dataset before training DALL·E 2. We trained these image classifiers in-house and are continuing to study the effects of dataset filtering on our trained model.

To train our image classifiers, we reused an approach that we had previously employed to filter training data for GLIDE⁠(opens in a new window). The basic steps to this approach are as follows: first, we create a specification for the image categories we would like to label; second, we gather a few hundred positive and negative examples for each category; third, we use an active learning procedure to gather more data and improve the precision/recall trade-off; and finally, we run the resulting classifier on the entire dataset with a conservative classification threshold to favor recall over precision. To set these thresholds, we prioritized filtering out all of the bad data over leaving in all of the good data. This is because we can always fine-tune our model with more data later to teach it new things, but it’s much harder to make the model forget something that it has already learned.

During the active learning phase, we iteratively improved our classifiers by gathering human labels for potentially difficult or misclassified images. Notably, we used two active learning techniques to choose images from our dataset (which contains hundreds of millions of unlabeled images) to present to humans for labeling. First, to reduce our classifier’s false positive rate (i.e., the frequency with which it misclassifies a benign image as violent or sexual), we assigned human labels to images that the current model classified as positive. For this step to work well, we tuned our classification threshold for nearly 100% recall but a high false-positive rate; this way, our labelers were mostly labeling truly negative cases. While this technique helps to reduce false positives and reduces the need for labelers to look at potentially harmful images, it does not help find more positive cases that the model is currently missing.