With the increasing role of Natural Language Processing (NLP) in various
applications, challenges concerning bias and stereotype perpetuation are
accentuated, which often leads to hate speech and harm. Despite existing
studies on sexism and misogyny, issues like homophobia and transphobia remain
underexplored and often adopt binary perspectives, putting the safety of
LGBTQIA+ individuals at high risk in online spaces. In this paper, we assess
the potential harm caused by sentence completions generated by English large
language models (LLMs) concerning LGBTQIA+ individuals. This is achieved using
QueerBench, our new assessment framework, which employs a template-based
approach and a Masked Language Modeling (MLM) task. The analysis indicates that
large language models tend to exhibit discriminatory behaviour more frequently
towards individuals within the LGBTQIA+ community, reaching a difference gap of
7.2% in the QueerBench score of harmfulness.

通过使用我们的新评估框架 QueerBench，本文评估了英语大语言模型（LLMs）生成的句子完成对 LGBTQIA + 个体可能造成的潜在危害，并表明大型语言模型在对 LGBTQIA + 社群中的个体表现出更频繁的歧视行为，使得 QueerBench 有害性得分在 7.2% 的差距内增加。

QueerBench：度量语言模型对酷儿身份的歧视

QueerBench: Quantifying Discrimination in Language Models Toward Queer  Identities

LLMs are increasingly transforming our digital ecosystem, but they often
inherit societal biases learned from their training data, for instance
stereotypes associating certain attributes with specific identity groups. While
whether and how these biases are mitigated may depend on the specific use
cases, being able to effectively detect instances of stereotype perpetuation is
a crucial first step. Current methods to assess presence of stereotypes in
generated language rely on simple template or co-occurrence based measures,
without accounting for the variety of sentential contexts they manifest in. We
argue that understanding the sentential context is crucial for detecting
instances of generalization. We distinguish two types of generalizations: (1)
language that merely mentions the presence of a generalization ("people think
the French are very rude"), and (2) language that reinforces such a
generalization ("as French they must be rude"), from non-generalizing context
("My French friends think I am rude"). For meaningful stereotype evaluations,
we need to reliably distinguish such instances of generalizations. We introduce
the new task of detecting generalization in language, and build GeniL, a
multilingual dataset of over 50K sentences from 9 languages (English, Arabic,
Bengali, Spanish, French, Hindi, Indonesian, Malay, and Portuguese) annotated
for instances of generalizations. We demonstrate that the likelihood of a
co-occurrence being an instance of generalization is usually low, and varies
across different languages, identity groups, and attributes. We build
classifiers to detect generalization in language with an overall PR-AUC of
58.7, with varying degrees of performance across languages. Our research
provides data and tools to enable a nuanced understanding of stereotype
perpetuation, a crucial step towards more inclusive and responsible language
technologies.

通过构建多语言数据集 GeniL，我们介绍了检测语言中概括化的新任务，其对多样的上下文进行了可靠区分，从而有助于深入理解刻板印象的永久化，这是实现更包容、负责任的语言技术的关键一步。