  Artificial writing is permeating our lives due to recent advances in
large-scale, transformer-based language models (LMs) such as BERT, its
variants, GPT-2/3, and others. Using them as pre-trained models and fine-tuning
them for specific tasks, researchers have extended state of the art for many
NLP tasks and shown that they capture not only linguistic knowledge but also
retain general knowledge implicitly present in the data. Unfortunately, LMs
trained on unfiltered text corpora suffer from degenerated and biased
behaviour. While this is well established, we show that recent LMs also contain
human-like biases of what is right and wrong to do, some form of ethical and
moral norms of the society -- they bring a "moral direction" to surface. That
is, we show that these norms can be captured geometrically by a direction,
which can be computed, e.g., by a PCA, in the embedding space, reflecting well
the agreement of phrases to social norms implicitly expressed in the training
texts and providing a path for attenuating or even preventing toxic
degeneration in LMs. Being able to rate the (non-)normativity of arbitrary
phrases without explicitly training the LM for this task, we demonstrate the
capabilities of the "moral direction" for guiding (even other) LMs towards
producing normative text and showcase it on RealToxicityPrompts testbed,
preventing the neural toxic degeneration in GPT-2.

Computation and Language;Computers and Society