From 'Showgirls' to 'Performers': Fine-tuning with Gender-inclusive Language for Bias Reduction in LLMs
Insight SFI Research Centre for Data Analytics（Insight SFI数据 analytics研究中心）；School of Information and Communication Studies（信息与传播研究学院）；University College Dublin（都柏林大学学院）
Marion Bartl, Susan Leavy
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10 pages, 5 tables; to appear in Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing at ACL 2024
Gender bias is not only prevalent in Large Language Models (LLMs) and their training data, but also firmly ingrained into the structural aspects of language itself. Therefore, adapting linguistic structures within LLM training data to promote gender-inclusivity can make gender representations within the model more inclusive. The focus of our work are gender-exclusive affixes in English, such as in 'show-girl' or 'man-cave', which can perpetuate gender stereotypes and binary conceptions of gender. We use an LLM training dataset to compile a catalogue of 692 gender-exclusive terms along with gender-neutral variants and from this, develop a gender-inclusive fine-tuning dataset, the 'Tiny Heap'. Fine-tuning three different LLMs with this dataset, we observe an overall reduction in gender-stereotyping tendencies across the models. Our approach provides a practical method for enhancing gender inclusivity in LLM training data and contributes to incorporating queer-feminist linguistic activism in bias mitigation research in NLP.
Adapting Psycholinguistic Research for LLMs: Gender-inclusive Language in a Coreference Context
Insight SFI Research Centre for Data Analytics（Insight SFI数据 analytics研究中心）；School of Information and Communication Studies（信息与传播研究学院）；School of Mathematics and Statistics（数学与统计学院）；University College Dublin（都柏林大学学院）
Gender-inclusive language is important for achieving gender equality in languages with gender inflections, such as German. While stirring some controversy, it is increasingly adopted by companies and political institutions. A handful of tools have been developed to help people use gender-inclusive language by identifying instances of the generic masculine and providing suggestions for more inclusive reformulations. In this report, we define the underlying tasks in terms of natural language processing, and present a dataset and measures for benchmarking them. We also present a model that implem…
University of Maryland（马里兰大学）；Microsoft Research（微软研究院）
Correctly resolving textual mentions of people fundamentally entails making inferences about those people. Such inferences raise the risk of systemic biases in coreference resolution systems, including biases that can harm binary and non-binary trans and cis stakeholders. To better understand such biases, we foreground nuanced conceptualizations of gender from sociology and sociolinguistics, and develop two new datasets for interrogating bias in crowd annotations and in existing coreference resolution systems. Through these studies, conducted on English text, we confirm that without acknowledg…
Università Luigi Bocconi（博科尼大学）；University of South Carolina（南卡罗来纳大学）
The world of pronouns is changing. From a closed class of words with few members to a much more open set of terms to reflect identities. However, Natural Language Processing (NLP) is barely reflecting this linguistic shift, even though recent work outlined the harms of gender-exclusive language technology. Particularly problematic is the current modeling 3rd person pronouns, as it largely ignores various phenomena like neopronouns, i.e., pronoun sets that are novel and not (yet) widely established. This omission contributes to the discrimination of marginalized and underrepresented groups, e.g…
University of California, Santa Barbara（加州大学圣巴巴拉分校）；Google Research（谷歌研究院）
Responsible development of technology involves applications being inclusive of the diverse set of users they hope to support. An important part of this is understanding the many ways to refer to a person and being able to fluently change between the different forms as needed. We perform a case study on the singular they, a common way to promote gender inclusion in English. We define a re-writing task, create an evaluation benchmark, and show how a model can be trained to produce gender-neutral English with <1% word error rate with no human-labeled data. We discuss the practical applications an…
The rise of concern around Natural Language Processing (NLP) technologies containing and perpetuating social biases has led to a rich and rapidly growing area of research. Gender bias is one of the central biases being analyzed, but to date there is no comprehensive analysis of how "gender" is theorized in the field. We survey nearly 200 articles concerning gender bias in NLP to discover how the field conceptualizes gender both explicitly (e.g. through definitions of terms) and implicitly (e.g. through how gender is operationalized in practice). In order to get a better idea of emerging trajec…
Yet another algorithmic bias: A Discursive Analysis of Large Language Models Reinforcing Dominant Discourses on Gender and Race
Instituto de Estudos da Linguagem (IEL), Universidade Estadual de Campinas (UNICAMP)（语言研究学院（IEL），Campinas州立大学（UNICAMP））；Instituto de Computação (IC), Universidade Estadual de Campinas (UNICAMP)（计算学院（IC），Campinas州立大学（UNICAMP））；Instituto de Informática, Universidade Federal de Goiás (UFG)（信息学院，戈亚斯联邦大学（UFG））
Analyzing Diversity in Healthcare LLM Research: A Scientometric Perspective
Massachusetts Institute of Technology（麻省理工学院）；Universidad del Cauca（考卡大学）；University of Michigan（密歇根大学）；Universidad de Chile（智利大学）；Universidad de Concepción（康塞普西翁大学）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Mass General Brigham, Harvard Medical School（麻省总医院布里格姆医疗系统，哈佛医学院）
Recent years have seen an increasing need for gender-neutral and inclusive language. Within the field of NLP, there are various mono- and bilingual use cases where gender inclusive language is appropriate, if not preferred due to ambiguity or uncertainty in terms of the gender of referents. In this work, we present a rule-based and a neural approach to gender-neutral rewriting for English along with manually curated synthetic data (WinoBias+) and natural data (OpenSubtitles and Reddit) benchmarks. A detailed manual and automatic evaluation highlights how our NeuTral Rewriter, trained on data g…
Mind the Inclusivity Gap: Multilingual Gender-Neutral Translation Evaluation with mGeNTE
Fondazione Bruno Kessler（布鲁诺·科塞尔基金会）；Instituto de Telecomunicações（电信研究所）；DIT Forlì - University of Bologna（福利大学迪提学院）；Data Science Group - University of Hamburg（汉堡大学数据科学组）；University of Trento（特伦托大学）；LT 3 - Ghent University（根特大学LT 3）
ADAPT Centre, Trinity College Dublin（都柏林圣三一学院ADAPT中心）；ADAPT Centre, Dublin City University（都柏林城市大学ADAPT中心）；Microsoft（微软）；Tilburg University（蒂尔堡大学）
Gender bias is a frequent occurrence in NLP-based applications, especially pronounced in gender-inflected languages. Bias can appear through associations of certain adjectives and animate nouns with the natural gender of referents, but also due to unbalanced grammatical gender frequencies of inflected words. This type of bias becomes more evident in generating conversational utterances where gender is not specified within the sentence, because most current NLP applications still work on a sentence-level context. As a step towards more inclusive NLP, this paper proposes an automatic and general…
Coreference resolution is an important component in analyzing narrative text from administrative data (e.g., clinical or police sources). However, existing coreference models trained on general language corpora suffer from poor transferability due to domain gaps, especially when they are applied to gender-inclusive data with lesbian, gay, bisexual, and transgender (LGBT) individuals. In this paper, we analyzed the challenges of coreference resolution in an exemplary form of administrative text written in English: violent death narratives from the USA's Centers for Disease Control's (CDC) Natio…
American University of Beirut（贝鲁特美国大学）；Beirut, Lebanon（黎巴嫩贝鲁特）
Language has a profound impact on our thoughts, perceptions, and conceptions of gender roles. Gender-inclusive language is, therefore, a key tool to promote social inclusion and contribute to achieving gender equality. Consequently, detecting and mitigating gender bias in texts is instrumental in halting its propagation and societal implications. However, there is a lack of gender bias datasets and lexicons for automating the detection of gender bias using supervised and unsupervised machine learning (ML) and natural language processing (NLP) techniques. Therefore, the main contribution of thi…
taz2024full: Analysing German Newspapers for Gender Bias and Discrimination across Decades
Faculty for Computer Science and Mathematics, Hochschule München University of Applied Sciences（计算机科学与数学系，慕尼黑应用科学大学）；Department of Statistics, LMU Munich（统计学系，慕尼黑大学）；Munich Center for Machine Learning (MCML), LMU Munich（慕尼黑机器学习中心（MCML），慕尼黑大学）
Cross-Lingual Probing and Community-Grounded Analysis of Gender Bias in Low-Resource Bengali
Department of Computer Science（计算机科学系）；AIUB, Dhaka, Bangladesh；Department of Information Technology（信息科技系）；Wilmington University, Delaware, USA；WUST, Virginia, USA；Faculty of Information Science（信息科学系）；Technology Multimedia University, Melaka, Malaysia（技术多媒体大学, Melaka, Malaysia）；Centre for Intelligent Cloud Computing（智能云计算中心）
Alexandria: A Multi-Domain Dialectal Arabic Machine Translation Dataset for Culturally Inclusive and Linguistically Diverse LLMs
The University of British Columbia（不列颠哥伦比亚大学）；Canada Research Chair in NLP and ML（自然语言处理和机器学习研究主席）；Mohammed VI Polytechnic University（穆莱·阿卜杜勒阿齐兹国王理工学院）；Birzeit University（比尔泽特大学）；Western Michigan University（西部密歇根大学）；Tuwaiq Academy（图瓦伊克学院）；King Khalid University（国王卡利德大学）；American University of Beirut（贝鲁特美国大学）；Ibb University（伊卜大学）；University of Hail（海勒大学）；University of Technology and Applied Sciences（技术与应用科学大学）；Arab Open University（阿拉伯开放大学）；Minia University（米尼亚大学）；Nantes University（南特大学）；Prince Sultan University（沙特王子大学）；Umm Al-Qura University（乌姆·阿勒·卡拉大学）；University of Nouakchott（努尔人大学）；Fatabyyano（法塔比亚诺）；Independent Researcher（独立研究者）；Hadhramout University（哈德拉姆大学）；Cairo University（开罗大学）；Misurata University（米斯拉塔大学）；Al-Balqa Applied University（巴勒斯坦应用大学）；University of Khartoum（喀土穆大学）；Sultan Qaboos Higher Centre for Culture and Science（穆罕默德·本·拉希德·阿勒马克图姆文化与科学高级中心）；Arab Center for Research and Policy Studies（阿拉伯研究中心）；Hamad Bin Khalifa University（哈马德·本·哈利法大学）；Institut Supérieur du Numérique（数字高级学院）
Speech is the fundamental means of communication between humans. The advent of AI and sophisticated speech technologies have led to the rapid proliferation of human-to-computer-based interactions, fueled primarily by Automatic Speech Recognition (ASR) systems. ASR systems normally take human speech in the form of audio and convert it into words, but for some users, it cannot decode the speech, and any output text is filled with errors that are incomprehensible to the human reader. These systems do not work equally for everyone and actually hinder the productivity of some users. In this paper, …
