Uncovering Latent Human Wellbeing in Language Model Embeddings
@misc{freire2024uncoveringlatenthumanwellbeing,
title={Uncovering Latent Human Wellbeing in Language Model Embeddings},
author={Pedro Freire and ChengCheng Tan and Adam Gleave and Dan Hendrycks and Scott Emmons},
year={2024},
eprint={2402.11777},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2402.11777},
}
February 18, 2024
Abstract
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.
