Reducing Non-Normative Text Generation from Language Models
Xiangyu Peng, Siyan Li, Spencer Frazier, Mark Riedl · 2020
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 judgements 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.