本文介绍了探索性的工作，介绍了以及在何种程度上对酷儿和跨性别者的偏见是用大型语言模型（LLM）（例如伯特）编码的。我们还提出了一种减少下游任务中这些偏见的方法：对由和/或关于酷儿人编写的数据进行填充。为了衡量抗Quase偏见，我们引入了一个新的基准数据集Winoqueer，以其他偏置检测基准测试，但要解决同性恐惧和跨性别偏见。我们发现伯特表现出明显的同性恋偏见，但是这种偏见可以通过finetuning bert对LGBTQ+社区成员撰写的自然语言语料库进行缓解。

语言可以用作再现和执行有害刻板印象和偏差的手段，并被分析在许多研究中。在本文中，我们对自然语言处理中的性别偏见进行了304篇论文。我们分析了社会科学中性别及其类别的定义，并将其连接到NLP研究中性别偏见的正式定义。我们调查了在对性别偏见的研究中应用的Lexica和数据集，然后比较和对比方法来检测和减轻性别偏见。我们发现对性别偏见的研究遭受了四个核心限制。 1）大多数研究将性别视为忽视其流动性和连续性的二元变量。 2）大部分工作都在单机设置中进行英语或其他高资源语言进行。 3）尽管在NLP方法中对性别偏见进行了无数的论文，但我们发现大多数新开发的算法都没有测试他们的偏见模型，并无视他们的工作的伦理考虑。 4）最后，在这一研究线上发展的方法基本缺陷涵盖性别偏差的非常有限的定义，缺乏评估基线和管道。我们建议建议克服这些限制作为未来研究的指导。

最近的研究揭示了NLP数据和模型中的不良偏见。但是，这些努力的重点是西方的社会差异，并且无法直接携带其他地质文化背景。在本文中，我们关注印度背景下的NLP公平。我们首先简要说明印度的社会差异斧头。我们为印度背景下的公平评估建立资源，并利用它们来证明沿着某些轴的预测偏见。然后，我们深入研究了地区和宗教的社会刻板印象，证明了其在Corpora＆Models中的普遍性。最后，我们概述了一个整体研究议程，以重新定义印度背景的NLP公平研究，考虑印度社会背景，弥合能力，资源和适应印度文化价值的技术差距。尽管我们在这里专注于“印度”，但可以在其他地理文化背景下进行重新连接化。

社会渗目偏见是自然语言处理的常见问题，影响其应用的公平性和完整性。在情绪分析中，这些偏差可能会破坏提到个人属性的文本的情感预测，即个人属性，即非偏见的人类读者会考虑中立。这种歧视可能对公共和私营部门的情感分析应用有很大的后果。例如，在线滥用和社交媒体平台的在线滥用和意见分析等应用中的不正确推断可能导致不必要的后索，例如错误的审查，以某些人口。在本文中，我们通过情感分析和毒性分类模型来解决残疾人PWD的歧视。我们提供对情绪和毒性分析模型的检查，详细了解它们如何辨别PWD。我们在情绪（比特）中展示了偏见识别试验，这是一个1,126句话的语料库，旨在探测残疾偏差的情绪分析模型。我们使用此语料库来展示四种广泛使用的情绪分析工具（TextBlob，Vader，Google云天然语言API和Distilbert）以及培训的两种毒性分析模型，以预测对拼图挑战的毒性评论（毒性评论分类和意外偏见毒性评论）。结果表明，所有在提及残疾的句子上表现出强烈的负面偏见。我们公开释放其他人的位语法，以识别任何情绪分析工具中的潜在偏见，以及更新要用作其他社会阶段变量的测试。

大型语言模型会产生类似人类的文本，这些文本推动了越来越多的应用。但是，最近的文献以及越来越多的现实世界观察表明，这些模型可以产生有毒，有偏见，不真实或其他有害的语言。尽管正在进行评估语言模型危害的工作，但要远见卓识转换出可能出现的危害可能会引起严格的基准。为了促进这种翻译，我们概述了六种表征有害文本的方式，这些方法在设计新基准时值得明确考虑。然后，我们将这些特征用作镜头来识别现有基准中的趋势和差距。最后，我们将它们应用于视角API的案例研究，这是一种毒性分类器，被广泛用于HARS基准。我们的特征提供了一块桥梁，可以在远见和有效评估之间转化。

对自然语言处理资源中的偏置模式的提高意识，如BERT，具有许多度量来量化“偏见”和“公平”。但是，如果没有完全不可能，请比较不同指标的结果和评估这些度量的作品仍然困难。我们调查了对预用语言模型的公平度量标准的现有文献，并通过实验评估兼容性，包括语言模型中的偏差，如在其下游任务中。我们通过传统文献调查和相关分析的混合来实现这一目标，以及运行实证评估。我们发现许多指标不兼容，高度依赖于（i）模板，（ii）属性和目标种子和（iii）选择嵌入式。这些结果表明，公平或偏见评估对情境化语言模型仍然具有挑战性，如果不是至少高度主观。为了提高未来的比较和公平评估，我们建议避免嵌入基于的指标并专注于下游任务中的公平评估。

News articles both shape and reflect public opinion across the political spectrum. Analyzing them for social bias can thus provide valuable insights, such as prevailing stereotypes in society and the media, which are often adopted by NLP models trained on respective data. Recent work has relied on word embedding bias measures, such as WEAT. However, several representation issues of embeddings can harm the measures' accuracy, including low-resource settings and token frequency differences. In this work, we study what kind of embedding algorithm serves best to accurately measure types of social bias known to exist in US online news articles. To cover the whole spectrum of political bias in the US, we collect 500k articles and review psychology literature with respect to expected social bias. We then quantify social bias using WEAT along with embedding algorithms that account for the aforementioned issues. We compare how models trained with the algorithms on news articles represent the expected social bias. Our results suggest that the standard way to quantify bias does not align well with knowledge from psychology. While the proposed algorithms reduce the~gap, they still do not fully match the literature.

We survey 146 papers analyzing "bias" in NLP systems, finding that their motivations are often vague, inconsistent, and lacking in normative reasoning, despite the fact that analyzing "bias" is an inherently normative process. We further find that these papers' proposed quantitative techniques for measuring or mitigating "bias" are poorly matched to their motivations and do not engage with the relevant literature outside of NLP. Based on these findings, we describe the beginnings of a path forward by proposing three recommendations that should guide work analyzing "bias" in NLP systems. These recommendations rest on a greater recognition of the relationships between language and social hierarchies, encouraging researchers and practitioners to articulate their conceptualizations of "bias"-i.e., what kinds of system behaviors are harmful, in what ways, to whom, and why, as well as the normative reasoning underlying these statements-and to center work around the lived experiences of members of communities affected by NLP systems, while interrogating and reimagining the power relations between technologists and such communities. Anne H. Charity Hudley. 2017. Language and Racialization. In Ofelia García, Nelson Flores, and Massimiliano Spotti, editors, The Oxford Handbook of Language and Society. Oxford University Press. Won Ik Cho, Ji Won Kim, Seok Min Kim, and Nam Soo Kim. 2019. On measuring gender bias in translation of gender-neutral pronouns. In Proceedings of the Workshop on Gender Bias in Natural Language Processing, pages 173-181, Florence, Italy.

Task agnostic generative pretraining (GPT) has recently proved promising for zero- and few-shot learning, gradually diverting attention from the expensive supervised learning paradigm. Although the community is accumulating knowledge as to capabilities of English-language autoregressive models such as GPT-3 adopting this generative approach, scholarship about these models remains acutely Anglocentric. Consequently, the community currently has serious gaps in its understanding of this class of models, their potential, and their societal impacts in diverse settings, linguistic traditions, and cultures. To alleviate this issue for Arabic, a collection of diverse languages and language varieties with more than $400$ million population, we introduce JASMINE, a suite of powerful Arabic autoregressive Transformer language models ranging in size between 300 million-13 billion parameters. We pretrain our new models with large amounts of diverse data (400GB of text) from different Arabic varieties and domains. We evaluate JASMINE extensively in both intrinsic and extrinsic settings, using a comprehensive benchmark for zero- and few-shot learning across a wide range of NLP tasks. We also carefully develop and release a novel benchmark for both automated and human evaluation of Arabic autoregressive models focused at investigating potential social biases, harms, and toxicity in these models. We aim to responsibly release our models with interested researchers, along with code for experimenting with them

Following the outbreak of a global pandemic, online content is filled with hate speech. Donald Trump's ''Chinese Virus'' tweet shifted the blame for the spread of the Covid-19 virus to China and the Chinese people, which triggered a new round of anti-China hate both online and offline. This research intends to examine China-related hate speech on Twitter during the two years following the burst of the pandemic (2020 and 2021). Through Twitter's API, in total 2,172,333 tweets hashtagged #china posted during the time were collected. By employing multiple state-of-the-art pretrained language models for hate speech detection, we identify a wide range of hate of various types, resulting in an automatically labeled anti-China hate speech dataset. We identify a hateful rate in #china tweets of 2.5% in 2020 and 1.9% in 2021. This is well above the average rate of online hate speech on Twitter at 0.6% identified in Gao et al., 2017. We further analyzed the longitudinal development of #china tweets and those identified as hateful in 2020 and 2021 through visualizing the daily number and hate rate over the two years. Our keyword analysis of hate speech in #china tweets reveals the most frequently mentioned terms in the hateful #china tweets, which can be used for further social science studies.

The field of cybersecurity is evolving fast. Experts need to be informed about past, current and - in the best case - upcoming threats, because attacks are becoming more advanced, targets bigger and systems more complex. As this cannot be addressed manually, cybersecurity experts need to rely on machine learning techniques. In the texutual domain, pre-trained language models like BERT have shown to be helpful, by providing a good baseline for further fine-tuning. However, due to the domain-knowledge and many technical terms in cybersecurity general language models might miss the gist of textual information, hence doing more harm than good. For this reason, we create a high-quality dataset and present a language model specifically tailored to the cybersecurity domain, which can serve as a basic building block for cybersecurity systems that deal with natural language. The model is compared with other models based on 15 different domain-dependent extrinsic and intrinsic tasks as well as general tasks from the SuperGLUE benchmark. On the one hand, the results of the intrinsic tasks show that our model improves the internal representation space of words compared to the other models. On the other hand, the extrinsic, domain-dependent tasks, consisting of sequence tagging and classification, show that the model is best in specific application scenarios, in contrast to the others. Furthermore, we show that our approach against catastrophic forgetting works, as the model is able to retrieve the previously trained domain-independent knowledge. The used dataset and trained model are made publicly available

随着19日的流行，对亚洲人，尤其是中国人的仇恨正在加剧。迫切需要有效地检测并防止对亚洲人的仇恨言论。在这项工作中，我们首先创建了Covid Hate-2022，这是一个带注释的数据集，其中包括2022年2月上旬提取的2,025条带注释的推文，根据特定标准进行了标签，我们介绍了仇恨和非讨厌推文的全面收集数据集。其次，我们根据相关数据集微调BERT模型，并演示与推文“清洁”有关的几种策略。第三，我们以各种以模型为中心和以数据为中心的方法调查了高级微调策略的性能，并且我们表明，这两种策略通常都改善了性能，而以数据为中心的策略则胜过其他策略，并且证明了可行性和有效性相关任务中以数据为中心的方法。

鉴于社交媒体消费的增加，估计社交媒体使用者的政治倾向是一个具有挑战性且越来越紧迫的问题。我们介绍了retweet-bert，这是一个简单且可扩展的模型，以估算Twitter用户的政治倾向。 retweet-bert利用转发网络结构和用户配置文件描述中使用的语言。我们的假设源于具有类似意识形态的人的网络和语言学的模式。 retweet-bert表现出对其他最先进的基线的竞争性能，在最近的两个Twitter数据集（COVID-19数据集和2020年美国总统选举数据集）中，达到96％-97％的宏观F1。我们还执行手动验证，以验证培训数据中不在培训数据中的用户的retweet-bert的性能。最后，在Covid-19的案例研究中，我们说明了Twitter上政治回声室的存在，并表明它主要存在于正确的倾斜用户中。我们的代码是开源的，我们的数据已公开可用。

我们研究了掩盖语言模型（MLMS）的任务无关内在和特定于任务的外在社会偏见评估措施之间的关系，并发现这两种评估措施之间仅存在弱相关性。此外，我们发现在下游任务进行微调期间，使用不同方法的MLMS DEBIAS进行了重新划分。我们确定两个培训实例中的社会偏见及其分配的标签是内在偏见评估测量值之间差异的原因。总体而言，我们的发现突出了现有的MLM偏见评估措施的局限性，并提出了使用这些措施在下游应用程序中部署MLM的担忧。

Objective. Chemical named entity recognition (NER) models have the potential to impact a wide range of downstream tasks, from identifying adverse drug reactions to general pharmacoepidemiology. However, it is unknown whether these models work the same for everyone. Performance disparities can potentially cause harm rather than the intended good. Hence, in this paper, we measure gender-related performance disparities of chemical NER systems. Materials and Methods. We develop a framework to measure gender bias in chemical NER models using synthetic data and a newly annotated dataset of over 92,405 words with self-identified gender information from Reddit. We applied and evaluated state-of-the-art biomedical NER models. Results. Our findings indicate that chemical NER models are biased. The results of the bias tests on the synthetic dataset and the real-world data multiple fairness issues. For example, for synthetic data, we find that female-related names are generally classified as chemicals, particularly in datasets containing many brand names rather than standard ones. For both datasets, we find consistent fairness issues resulting in substantial performance disparities between female- and male-related data. Discussion. Our study highlights the issue of biases in chemical NER models. For example, we find that many systems cannot detect contraceptives (e.g., birth control). Conclusion. Chemical NER models are biased and can be harmful to female-related groups. Therefore, practitioners should carefully consider the potential biases of these models and take steps to mitigate them.

在本文中，我们介绍了TweetNLP，这是社交媒体中自然语言处理（NLP）的集成平台。TweetNLP支持一套多样化的NLP任务，包括诸如情感分析和命名实体识别的通用重点领域，以及社交媒体特定的任务，例如表情符号预测和进攻性语言识别。特定于任务的系统由专门用于社交媒体文本的合理大小的基于变压器的语言模型（尤其是Twitter）提供动力，无需专用硬件或云服务即可运行。TweetNLP的主要贡献是：（1）使用适合社会领域的各种特定于任务的模型，用于支持社交媒体分析的现代工具包的集成python库；（2）使用我们的模型进行无编码实验的交互式在线演示；（3）涵盖各种典型社交媒体应用的教程。

最近受到在线叙述驱动的疫苗犹豫会大大降低了疫苗接种策略的功效，例如Covid-19。尽管医学界对可用疫苗的安全性和有效性达成了广泛的共识，但许多社交媒体使用者仍被有关疫苗的虚假信息淹没，并且柔和或不愿意接种疫苗。这项研究的目的是通过开发能够自动识别负责传播反疫苗叙事的用户的系统来更好地理解反疫苗情绪。我们引入了一个公开可用的Python软件包，能够分析Twitter配置文件，以评估该个人资料将来分享反疫苗情绪的可能性。该软件包是使用文本嵌入方法，神经网络和自动数据集生成的，并接受了数百万条推文培训。我们发现，该模型可以准确地检测出抗疫苗用户，直到他们推文抗Vaccine主题标签或关键字。我们还展示了文本分析如何通过检测Twitter和常规用户之间的抗疫苗传播器之间的道德和情感差异来帮助我们理解反疫苗讨论的示例。我们的结果将帮助研究人员和政策制定者了解用户如何成为反疫苗感以及他们在Twitter上讨论的内容。政策制定者可以利用此信息进行更好的针对性的运动，以揭露有害的反疫苗接种神话。

We demonstrate that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even becoming competitive with prior state-ofthe-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous nonsparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks. We also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora.

道德框架和情感会影响各种在线和离线行为，包括捐赠，亲环境行动，政治参与，甚至参与暴力抗议活动。自然语言处理中的各种计算方法（NLP）已被用来从文本数据中检测道德情绪，但是为了在此类主观任务中取得更好的性能，需要大量的手工注销训练数据。事实证明，以前对道德情绪注释的语料库已被证明是有价值的，并且在NLP和整个社会科学中都产生了新的见解，但仅限于Twitter。为了促进我们对道德修辞的作用的理解，我们介绍了道德基础Reddit语料库，收集了16,123个reddit评论，这些评论已从12个不同的子雷迪维特策划，由至少三个训练有素的注释者手工注释，用于8种道德情绪（即护理，相称性，平等，纯洁，权威，忠诚，瘦道，隐含/明确的道德）基于更新的道德基础理论（MFT）框架。我们使用一系列方法来为这种新的语料库（例如跨域分类和知识转移）提供基线道德句子分类结果。

由于BERT出现，变压器语言模型和转移学习已成为自然语言理解任务的最先进。最近，一些作品适用于特定领域的预训练，专制模型，例如科学论文，医疗文件等。在这项工作中，我们呈现RoberTuito，用于西班牙语中的用户生成内容的预先训练的语言模型。我们在西班牙语中培训了罗伯特托5亿推文。关于涉及用户生成文本的4个任务的基准测试显示，罗伯特托多于西班牙语的其他预先接受的语言模型。为了帮助进一步研究，我们将罗伯特多公开可在HuggingFace Model Hub上提供。