Logic Against Bias: Textual Entailment Mitigates Stereotypical Sentence Reasoning (2023)

Abstract: Due to their similarity-based learning objectives, pretrained sentence encoders often internalize stereotypical assumptions that reflect the social biases that exist within their training corpora. In this paper, we describe several kinds of stereotypes concerning different communities that are present in popular sentence representation models, including pretrained next sentence prediction and contrastive sentence representation models. We compare such models to textual entailment models that learn language logic for a variety of downstream language understanding tasks. By comparing strong pretrained models based on text similarity with textual entailment learning, we conclude that the explicit logic learning with textual entailment can significantly reduce bias and improve the recognition of social communities, without an explicit de-biasing process.

## introduction recent pretrained language models have achieved significant improvements on natural language understanding tasks @xcite @xcite @xcite . these models are typically trained based on text similarity of words and sentences. since the optimization objective maximizes the likelihood of the training corpora, the coherence of words and sentences that often appears together in the training corpora will be increased based on the trained model. however, since the training corpora are generated by humans, they can contain a large amount of social bias and stereotypes, including those concerning gender, race, and religion @xcite @xcite . the person is a doctor. the person is feminine. the person is masculine. ## related work recent advances in language modeling has followed the strategy of learning large-scale models on large-scale unannotated corpora with selfsupervised learning, including masked word and next sentence prediction @xcite @xcite , wrong word detection @xcite , and left-to-right language generation @xcite . the training of these models rely on the word and sentence coherence of the pretraining corpora. word-level language models are the foundation of sentence-level language encoders, including sen-tencebert @xcite , sim-cse @xcite , and diffcse @xcite , that were proposed for generating sentence embeddings with better representation abilities. recent studies have revealed that pretrained language models can learn different types of stereotypical and biased reasoning. @xcite investigated biased languages using wikipedia texts. @xcite surveyed stereotypical reasoning in word-level language prediction and co-reference resolution. @xcite probed language models with the sentiment analysis task and measured the different model behav-iors against different social groups. stereotypical reasoning against race, gender, profession, and religion were also evaluated on recent masked language models and sentence encoders in @xcite and @xcite . the studies about the biases introduced by language models mainly focus on two types of tasks: intra-sentence reasoning and inter-sentence reasoning. intra-sentence, or word-level, reasoning represents word and co-reference selection in a single sentence, which reveals the bias within word and context representations @xcite @xcite @xcite @xcite . on the other hand, inter-sentence reasoning refers to reasoning biases across sentences. more specifically, a set of given sentences may not have any logical relationship, but a similarity-based language model may be biased towards linking a subset of the sentences, reflecting the coherence bias of the pretraining corpora @xcite @xcite . recent studies have also investigated the social bias under multilingual settings @xcite @xcite . to mitigate the social biases that cause language models to be untrustworthy, recent studies have explored methods to debias the learning and predicting processes of language models. typical debiasing methods include counterfactual data augmentation @xcite @xcite , dropout regularization @xcite , self-debias @xcite , sentence embedding debias @xcite , and iterative nullspace projection @xcite . besides the regular similarity-based pretraining method applied by most language models, some sentence encoding models also employ natural language inference (nli) corpora to learn textual entailment @xcite . superivised simcse @xcite and sentencebert @xcite use entailment data as a part of the pretraining corpora, while other studies apply entailment models to handle downstream tasks, including fact-checking @xcite , relation extraction @xcite , and text classification @xcite . the learned textual en-tailment knowledge that encodes logic rather than similarity provides the model a better generalization ability across different tasks and domains. ## experiments 4.1 language understanding to ensure that the fairness of the entailment-based language model does not come from a lack of language understanding ability, we first show the zero-shot adaptation performance of the entailmentbased language models. on the mnli-mismatch task, the roberta model achieves 89.0% accuracy, and the deberta model achieves 83.4%. we compare different language models on other tasks in the glue benchmark @xcite , including qnli, qqp, rte, and sst2 tasks. for each task, we construct suppositions for classification according to the corresponding task description as shown in we compare the zero-shot adaptation performance of our entailment-based supposition (esp) language models with weakly supervised baseline models of different scales as follows: few-shot 350m models. we compare our task inputs supposition mnli {p, h} h is entailed by p. rte {p, h} h is entailed by p. qnli {p, q} the answer to q is entailed by p. qqp {x, y} x's answer is entailed by y's answer. sst2 {r} the movie is good is entailed by r. 79.9 84.5 90.1 82.9 ## conclusion in this work, we found that textual entailment learning reduces the bias of pretrained language models for sentence representation. we evaluated bert, roberta, and deberta-based pretrained, sim-cse, and entailment models on stereotype, profession, and emotion bias tests. the textual entailment models outperform other models with significantly lower bias without other explicit debiasing processes, while preserving the language modeling ability, which results in significantly better idealized context association test scores. by analyzing the sentence embeddings, we found that the models relying on textual entailment produce less biased results by learning logic and reducing the amount of text coherence knowledge retained from the pretraining corpora containing existing social biases. on the other hand, there are further limitations in the benchmarks we study in this work, as pointed out by @xcite that stereoset is not perfect. on the other hand, some words in the vocabulary collected by @xcite are rarely used, for example, "poetess" and "manageress". in future work, we will explore building more inclusive and comprehensive benchmarks to mitigate the limitations.