CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models

• URL: http://arxiv.org/abs/2010.00133v1
• Date: Wed, 30 Sep 2020 22:38:40 GMT
• Title: CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models
• Authors: Nikita Nangia, Clara Vania, Rasika Bhalerao, Samuel R. Bowman
• Abstract summary: We introduce the Crowd Stereotype Pairs benchmark (CrowS-Pairs) CrowS-Pairs has 1508 examples that cover stereotypes dealing with nine types of bias, like race, religion, and age. We find that all three of the widely-used sentences we evaluate substantially favor stereotypes in every category in CrowS-Pairs.
• Score: 30.582132471411263
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pretrained language models, especially masked language models (MLMs) have seen success across many NLP tasks. However, there is ample evidence that they use the cultural biases that are undoubtedly present in the corpora they are trained on, implicitly creating harm with biased representations. To measure some forms of social bias in language models against protected demographic groups in the US, we introduce the Crowdsourced Stereotype Pairs benchmark (CrowS-Pairs). CrowS-Pairs has 1508 examples that cover stereotypes dealing with nine types of bias, like race, religion, and age. In CrowS-Pairs a model is presented with two sentences: one that is more stereotyping and another that is less stereotyping. The data focuses on stereotypes about historically disadvantaged groups and contrasts them with advantaged groups. We find that all three of the widely-used MLMs we evaluate substantially favor sentences that express stereotypes in every category in CrowS-Pairs. As work on building less biased models advances, this dataset can be used as a benchmark to evaluate progress.

Related papers

• Spoken Stereoset: On Evaluating Social Bias Toward Speaker in Speech Large Language Models [50.40276881893513]
  This study introduces Spoken Stereoset, a dataset specifically designed to evaluate social biases in Speech Large Language Models (SLLMs) By examining how different models respond to speech from diverse demographic groups, we aim to identify these biases. The findings indicate that while most models show minimal bias, some still exhibit slightly stereotypical or anti-stereotypical tendencies.
  arXiv  Detail & Related papers  (2024-08-14T16:55:06Z)
• Who is better at math, Jenny or Jingzhen? Uncovering Stereotypes in Large Language Models [9.734705470760511]
  We use GlobalBias to study a broad set of stereotypes from around the world. We generate character profiles based on given names and evaluate the prevalence of stereotypes in model outputs.
  arXiv  Detail & Related papers  (2024-07-09T14:52:52Z)
• White Men Lead, Black Women Help? Benchmarking Language Agency Social Biases in LLMs [58.27353205269664]
  Social biases can manifest in language agency. We introduce the novel Language Agency Bias Evaluation benchmark. We unveil language agency social biases in 3 recent Large Language Model (LLM)-generated content.
  arXiv  Detail & Related papers  (2024-04-16T12:27:54Z)
• Stereotype Detection in LLMs: A Multiclass, Explainable, and Benchmark-Driven Approach [4.908389661988191]
  This paper introduces the Multi-Grain Stereotype (MGS) dataset, consisting of 51,867 instances across gender, race, profession, religion, and other stereotypes. We evaluate various machine learning approaches to establish baselines and fine-tune language models of different architectures and sizes. We employ explainable AI (XAI) tools, including SHAP, LIME, and BertViz, to assess whether the model's learned patterns align with human intuitions about stereotypes.
  arXiv  Detail & Related papers  (2024-04-02T09:31:32Z)
• Protected group bias and stereotypes in Large Language Models [2.1122940074160357]
  This paper investigates the behavior of Large Language Models (LLMs) in the domains of ethics and fairness. We find bias across minoritized groups, but in particular in the domains of gender and sexuality, as well as Western bias.
  arXiv  Detail & Related papers  (2024-03-21T00:21:38Z)
• Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale [61.555788332182395]
  We investigate the potential for machine learning models to amplify dangerous and complex stereotypes. We find a broad range of ordinary prompts produce stereotypes, including prompts simply mentioning traits, descriptors, occupations, or objects.
  arXiv  Detail & Related papers  (2022-11-07T18:31:07Z)
• How True is GPT-2? An Empirical Analysis of Intersectional Occupational Biases [50.591267188664666]
  Downstream applications are at risk of inheriting biases contained in natural language models. We analyze the occupational biases of a popular generative language model, GPT-2. For a given job, GPT-2 reflects the societal skew of gender and ethnicity in the US, and in some cases, pulls the distribution towards gender parity.
  arXiv  Detail & Related papers  (2021-02-08T11:10:27Z)
• UnQovering Stereotyping Biases via Underspecified Questions [68.81749777034409]
  We present UNQOVER, a framework to probe and quantify biases through underspecified questions. We show that a naive use of model scores can lead to incorrect bias estimates due to two forms of reasoning errors. We use this metric to analyze four important classes of stereotypes: gender, nationality, ethnicity, and religion.
  arXiv  Detail & Related papers  (2020-10-06T01:49:52Z)
• StereoSet: Measuring stereotypical bias in pretrained language models [24.020149562072127]
  We present StereoSet, a large-scale natural dataset in English to measure stereotypical biases in four domains. We evaluate popular models like BERT, GPT-2, RoBERTa, and XLNet on our dataset and show that these models exhibit strong stereotypical biases.
  arXiv  Detail & Related papers  (2020-04-20T17:14:33Z)
• Contrastive Examples for Addressing the Tyranny of the Majority [83.93825214500131]
  We propose to create a balanced training dataset, consisting of the original dataset plus new data points in which the group memberships are intervened. We show that current generative adversarial networks are a powerful tool for learning these data points, called contrastive examples.
  arXiv  Detail & Related papers  (2020-04-14T14:06:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.