Most previous studies on automatic recognition model for bipolar disorder (BD) were based on both social media and linguistic features. The present study investigates the possibility of adopting only language-based features, namely the syntax and morpheme collocation. We also examine the effect of gender on the results considering gender has long been recognized as an important modulating factor for mental disorders, yet it received little attention in previous linguistic models. The present study collects Twitter posts 3 months prior to the self-disclosure by 349 BD users (231 female, 118 male). We construct a set of syntactic patterns in terms of the word usage based on graph pattern construction and pattern attention mechanism. The factors examined are gender differences, syntactic patterns, and bipolar recognition performance. The performance indicates our F1 scores reach over 91% and outperform several baselines, including those using TF-IDF, LIWC and pre-trained language models (ELMO and BERT). The contributions of the present study are: (1) The features are contextualized, domain-agnostic, and purely linguistic. (2) The performance of BD recognition is improved by gender-enriched linguistic pattern features, which are constructed with gender differences in language usage.
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）
This paper addresses the task of user gender classification in social media, with an application to Twitter. The approach automatically predicts gender by leveraging observable information such as the tweet behavior, linguistic content of the user's Twitter feed and the celebrities followed by the user. This paper first evaluates linguistic content based features using LIWC dictionary and popular neighborhood features using Wikipedia and Freebase. Then augments both features which yielded a significant increase in the accuracy for gender prediction. Results show that rich linguistic features c…
How desirable is alignment between LLMs and linguistically diverse human users?
Humboldt-Universität zu Berlin（柏林洪堡大学）；Berlin School of Mind and Brain（柏林心智与大脑学院）；Einstein Center for Neurosciences Berlin（柏林爱因斯坦神经科学中心）；Technische Universität Berlin（柏林工业大学）；German Research Center for Artificial Intelligence（德国人工智能研究中心）
CENIDET（墨西哥国家数字教育技术中心）；COLMEX（墨西哥学院）；IIM（印度管理研究所）；Université de Montréal（蒙特利尔大学）；EBSI（图书馆与信息科学学院）；Université d’Avignon（阿维尼翁大学）；LIA（阿维尼翁计算机科学实验室）
This work aims to evaluate the ability that both probabilistic and state-of-the-art vector space modeling (VSM) methods provide to well known machine learning algorithms to identify social network documents to be classified as aggressive, gender biased or communally charged. To this end, an exploratory stage was performed first in order to find relevant settings to test, i.e. by using training and development samples, we trained multiple algorithms using multiple vector space modeling and probabilistic methods and discarded the less informative configurations. These systems were submitted to t…
Analysing Differences in Persuasive Language in LLM-Generated Text: Uncovering Stereotypical Gender Patterns
Department of Computer Science, Aarhus University（阿arhus大学计算机科学系）；AMD Silo AI；University of Tokyo（东京大学）；IT University of Copenhagen（哥本哈根IT大学）；Department of Computer Science, University of Copenhagen（哥本哈根大学计算机科学系）
Inclusivity in Large Language Models: Personality Traits and Gender Bias in Scientific Abstracts
Information Sciences Institute, University of Southern California（南加州大学信息科学学院）；Iovine and Young Academy, University of Southern California（南加州大学约万与杨学院）；In Vivo Group
BIPOLAR: Polarization-based granular framework for LLM bias evaluation
Institute for Research and Applications of Fuzzy Modeling, University of Ostrava（模糊建模研究与应用研究所，奥斯特拉瓦大学）；Institute of Computer Science, Faculty of Philosophy and Science, Silesian University in Opava（计算机科学研究所，哲学与科学学院，奥帕瓦西里西亚大学）
To understand what kinds of linguistic knowledge are encoded by pretrained Chinese language models (LMs), we introduce the benchmark of Sino LINGuistics (SLING), which consists of 38K minimal sentence pairs in Mandarin Chinese grouped into 9 high-level linguistic phenomena. Each pair demonstrates the acceptability contrast of a specific syntactic or semantic phenomenon (e.g., The keys are lost vs. The keys is lost), and an LM should assign lower perplexity to the acceptable sentence. In contrast to the CLiMP dataset (Xiang et al., 2021), which also contains Chinese minimal pairs and was create…
Qatar Computing Research Institute（卡塔尔计算研究所）；Faculty of Computer Science, Dalhousie University（达尔豪斯大学计算机学院）；School of Engineering and Science, Steven Institute of Technology（史蒂文斯理工学院工程与科学学院）
The opacity of deep neural networks remains a challenge in deploying solutions where explanation is as important as precision. We present ConceptX, a human-in-the-loop framework for interpreting and annotating latent representational space in pre-trained Language Models (pLMs). We use an unsupervised method to discover concepts learned in these models and enable a graphical interface for humans to generate explanations for the concepts. To facilitate the process, we provide auto-annotations of the concepts (based on traditional linguistic ontologies). Such annotations enable development of a l…
Gender Bias in LLM Hiring Decisions: Evidence from a Japanese Context and Evaluation of Mitigation Strategies
Shibaura Institute of Technology, Tokyo, Japan（Shibaura技术学院，东京，日本）；Amsterdam University of Applied Sciences, Amsterdam, Netherlands（阿姆斯特丹应用科学大学，阿姆斯特丹，荷兰）；University of Pennsylvania, Philadelphia, USA（宾夕法尼亚大学，费城，美国）；Carnegie Mellon University, Pittsburgh, USA（卡内基梅隆大学，匹兹堡，美国）；Keio University, Tokyo, Japan（庆应大学，东京，日本）
The Voice Behind the Words: Quantifying Intersectional Bias in SpeechLLMs
1 Department of Speech, Music；Hearing, KTH Royal Institute of Technology, Sweden 2 Centre for Speech Technology Research, University of Edinburgh, UK 3 Texas A\&M University, USA
Although large pre-trained language models have achieved great success in many NLP tasks, it has been shown that they reflect human biases from their pre-training corpora. This bias may lead to undesirable outcomes when these models are applied in real-world settings. In this paper, we investigate the bias present in monolingual BERT models across a diverse set of languages (English, Greek, and Persian). While recent research has mostly focused on gender-related biases, we analyze religious and ethnic biases as well and propose a template-based method to measure any kind of bias, based on sent…
University of Antioquia（安蒂奥基亚大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）
The interest in demographic information retrieval based on text data has increased in the research community because applications have shown success in different sectors such as security, marketing, heath-care, and others. Recognition and identification of demographic traits such as gender, age, location, or personality based on text data can help to improve different marketing strategies. For instance it makes it possible to segment and to personalize offers, thus products and services are exposed to the group of greatest interest. This type of technology has been discussed widely in document…
