Uncovering Latent Human Wellbeing in Language Model Embeddings
Pedro Freire ChengCheng Tan Adam Gleave Dan Hendrycks Scott Emmons
Do language models implicitly learn a concept of human wellbeing? We explore
this through the ETHICS Utilitarianism task, assessing if scaling enhances
pretrained models' representations. Our initial finding reveals that, without
any prompt engineering or finetuning, the leading principal component from
OpenAI's text-embedding-ada-002 achieves 73.9% accuracy. This closely matches
the 74.6% of BERT-large finetuned on the entire ETHICS dataset, suggesting
pretraining conveys some understanding about human wellbeing. Next, we consider
four language model families, observing how Utilitarianism accuracy varies with
increased parameters. We find performance is nondecreasing with increased model
size when using sufficient numbers of principal components.