Large-scale, transformer-based language models such as GPT-2 are pretrained on diverse corpora scraped from the internet. Consequently, they are prone to generating non-normative text (i.e. in violation of social norms). We introduce a technique for fine-tuning GPT-2, using a policy gradient reinforcement learning technique and a normative text classifier to produce reward and punishment values. We evaluate our technique on five data sets using automated and human participant experiments. The normative text classifier is 81-90% accurate when compared to gold-standard human judgments of normative and non-normative generated text. Our normative fine-tuning technique is able to reduce non-normative text by 27-61%, depending on the data set.
@article{arxiv.2001.08764,
title = {Reducing Non-Normative Text Generation from Language Models},
author = {Xiangyu Peng and Siyan Li and Spencer Frazier and Mark Riedl},
journal= {arXiv preprint arXiv:2001.08764},
year = {2020}
}