Generic Overgeneralization in Pre-trained Language Models (2022)

Abstract: Generic statements such as “ducks lay eggs” make claims about kinds, e.g., ducks as a category. The generic overgeneralization effect refers to the inclination to accept false universal generalizations such as “all ducks lay eggs” or “all lions have manes” as true. In this paper, we investigate the generic overgeneralization effect in pre-trained language models experimentally. We show that pre-trained language models suffer from overgeneralization and tend to treat quantified generic statements such as “all ducks lay eggs” as if they were true generics. Furthermore, we demonstrate how knowledge embedding methods can lessen this effect by injecting factual knowledge about kinds into pre-trained language models. To this end, we source factual knowledge about two types of generics, minority characteristic generics and majority characteristic generics, and inject this knowledge using a knowledge embedding model. Our results show that knowledge injection reduces, but does not eliminate, generic overgeneralization, and that majority characteristic generics of kinds are more susceptible to overgeneralization bias.

## introduction generics are sentences such as "tigers have stripes" that express generalizations about kinds, although they are not universal or without exceptions. for example, there are albino tigers that do not have stripes. even though there are exceptions, generics are regarded as true. however, universally quantified statements such as "all ducks lay eggs" should be perceived as false as they can easily be invalidated because the quantifier all does not allow exceptions; it is only mature female ducks that are capable of laying eggs. empirical data from linguistics studies show that children and adults often tend to treat quantified pre-trained language models (plms) such as bert @xcite and roberta @xcite learn useful language representation from large-scale unstructured text and are able to store information about the world in their parameters @xcite @xcite . in this paper, we investigate the gog effect in plms, and ask if embedding factual knowledge about kinds during pre-training can reduce this effect. first, we investigate if plms can improve the classification accuracy of distinguishing between generic and non-generic statements. we construct datasets of minority and majority characteristic generics of kinds, and use these to train and evaluate a baseline model and the plms. results show that the pre-trained language models outperform the baseline on the classification task. we also demonstrate that the fine-tuned language models classify quantified statements such as "all ducks lay eggs" as generics, indicating that the models do overgeneralize and thus exhibit the gog effect. next, we define a masked word prediction task to test if the plms exhibit the gog effect by observing if the plms predict quantifiers to fill the masked words. for example, we ask plms to fill the masked position in a statement such as "[mask] lions have manes" ( given these observations, we ask whether injecting factual knowledge about kinds during language model pre-training reduces the gog effect. we source factual knowledge about kinds from as-cent kb @xcite , a knowledge base which contains facet-enriched assertions together with their associated context. the knowledge from ascent kb is injected into the plms using ke-pler @xcite , a model for embedding knowledge into plms using entity descriptions and entity relations data. experimental results suggest that the injected knowledge lessens the gog effect, but does not eliminate it completely. our contributions are: (i) we introduce new datasets for evaluating generic overgeneralization in plms, (ii) we demonstrate that plms exhibit the gog effect, (iii) we show that embedding factual knowledge can reduce the gog effect, and (iv) our results suggest that majority characteristic generics are more susceptible to overgeneralization bias. to the best of our knowledge, we present the first work investigating generic overgeneralization in plms. ## the gog effect evaluation task we introduce the gog effect evaluation task, describe how the datasets were created, and discuss how the factual knowledge about kinds that can be used for knowledge injection was extracted. ## probing the injected knowledge the results reported in the previous section warrant us to probe the injected knowledge in order to determine if the plms "understand" the injected minority generics majority generics model f1 accuracy f1 accuracy bi-lstm 0.80 0.83 0.82 0.86 bert 0.88 0.90 0.89 0.91 roberta 0.90 0.93 0.92 0.95 factual knowledge. for example, do the plms understand that it is only mature, male lions that can have manes? furthermore, do plms, with factual knowledge, correctly predict relevant tokens that could make quantified generic statements true? ## conclusion we investigated the generic overgeneralization (gog) effect in plms and demonstrated that plms do overgeneralize and treat quantified statements as if they were generics. we introduced datasets on minority and majority characteristic generics that can be used to evaluate the gog effect, as well as a source of factual knowledge about kinds to evaluate plm knowledge embedding methods. our results suggest that knowledge injection reduces the gog effect in plms but does not eliminate it, and that majority characteristic generic statements are more susceptible to overgeneralization bias. probing the models after knowledge injection, we were able to determine which quantifiers make minority or majority characteristic generics to remain as true quantified generic statements and which quantifiers make the generics to become non-generic statements. our paper makes the case for future research on methods for injecting commonsense into plms more effectively so that they can perform better natural language inference based on the knowledge presented. this would be an important step towards advancing commonsense reasoning in plms.