Pretrained neural language models (LMs) are prone to generating racist, sexist, or otherwise toxic language which hinders their safe deployment. We investigate the extent to which pretrained LMs can be prompted to generate toxic language, and the effectiveness of controllable text generation algorithms at preventing such toxic degeneration. We create and release RealToxicityPrompts, a dataset of 100K naturally occurring, sentence-level prompts derived from a large corpus of English web text, paired with toxicity scores from a widely-used toxicity classifier. Using RealToxicityPrompts, we find that pretrained LMs can degenerate into toxic text even from seemingly innocuous prompts. We empirically assess several controllable generation methods, and find that while data- or compute-intensive methods (e.g., adaptive pretraining on non-toxic data) are more effective at steering away from toxicity than simpler solutions (e.g., banning “bad” words), no current method is failsafe against neural toxic degeneration. To pinpoint the potential cause of such persistent toxic degeneration, we analyze two web text corpora used to pretrain several LMs (including GPT-2; Radford et. al, 2019), and find a significant amount of offensive, factually unreliable, and otherwise toxic content. Our work provides a test bed for evaluating toxic generations by LMs and stresses the need for better data selection processes for pretraining.
Although they are the backbone of many modern NLP systems (Devlin et al., 2019; Radford et al., 2019; Raffel et al., 2019) , language models (LMs) pretrained on large web text corpora suffer from degenerate and biased behavior (Sheng et al., 2019; Wallace et al., 2019) . As illustrated in Figure 1 , they can easily degenerate into toxicity, even without explicitly toxic prompts, which hinders their Figure 1: Non-toxic examples from REALTOXICI-TYPROMPTS, a new testbed for evaluating neural generations and their toxicity. Despite not containing any toxic language as measured by PERSPECTIVE API, these prompts cause several pretrained LMs to systematically generate highly toxic text (shown in Table 17 in Appendix §E).