An Empirical Study of Gendered Stereotypes in Emotional Attributes for Bangla in Multilingual Large Language Models

• URL: http://arxiv.org/abs/2407.06432v1
• Date: Mon, 8 Jul 2024 22:22:15 GMT
• Title: An Empirical Study of Gendered Stereotypes in Emotional Attributes for Bangla in Multilingual Large Language Models
• Authors: Jayanta Sadhu, Maneesha Rani Saha, Rifat Shahriyar,
• Abstract summary: Women are associated with emotions like empathy, fear, and guilt, while men are linked to anger, bravado, and authority. We show the existence of gender bias in the context of emotions in Bangla through analytical methods. All of our resources including code and data are made publicly available to support future research on Bangla NLP.
• Score: 2.98683507969764
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The influence of Large Language Models (LLMs) is rapidly growing, automating more jobs over time. Assessing the fairness of LLMs is crucial due to their expanding impact. Studies reveal the reflection of societal norms and biases in LLMs, which creates a risk of propagating societal stereotypes in downstream tasks. Many studies on bias in LLMs focus on gender bias in various NLP applications. However, there's a gap in research on bias in emotional attributes, despite the close societal link between emotion and gender. This gap is even larger for low-resource languages like Bangla. Historically, women are associated with emotions like empathy, fear, and guilt, while men are linked to anger, bravado, and authority. This pattern reflects societal norms in Bangla-speaking regions. We offer the first thorough investigation of gendered emotion attribution in Bangla for both closed and open source LLMs in this work. Our aim is to elucidate the intricate societal relationship between gender and emotion specifically within the context of Bangla. We have been successful in showing the existence of gender bias in the context of emotions in Bangla through analytical methods and also show how emotion attribution changes on the basis of gendered role selection in LLMs. All of our resources including code and data are made publicly available to support future research on Bangla NLP. Warning: This paper contains explicit stereotypical statements that many may find offensive.

Related papers

• Revealing and Reducing Gender Biases in Vision and Language Assistants (VLAs) [82.57490175399693]
  We study gender bias in 22 popular image-to-text vision-language assistants (VLAs) Our results show that VLAs replicate human biases likely present in the data, such as real-world occupational imbalances. To eliminate the gender bias in these models, we find that finetuning-based debiasing methods achieve the best tradeoff between debiasing and retaining performance on downstream tasks.
  arXiv  Detail & Related papers  (2024-10-25T05:59:44Z)
• Social Bias in Large Language Models For Bangla: An Empirical Study on Gender and Religious Bias [2.98683507969764]
  We examine two types of social biases in LLM generated outputs for Bangla language. This is the first work of such kind involving bias assessment of LLMs for Bangla to the best of our knowledge. All our code and resources are publicly available for the progress of bias related research in Bangla NLP.
  arXiv  Detail & Related papers  (2024-07-03T22:45:36Z)
• 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)
• Angry Men, Sad Women: Large Language Models Reflect Gendered Stereotypes in Emotion Attribution [20.21748776472278]
  We investigate whether emotions are gendered, and whether these variations are based on societal stereotypes. We find that all models consistently exhibit gendered emotions, influenced by gender stereotypes. Our study sheds light on the complex societal interplay between language, gender, and emotion.
  arXiv  Detail & Related papers  (2024-03-05T17:04:05Z)
• Gender Bias in Large Language Models across Multiple Languages [10.068466432117113]
  We examine gender bias in large language models (LLMs) generated for different languages. We use three measurements: 1) gender bias in selecting descriptive words given the gender-related context. 2) gender bias in selecting gender-related pronouns (she/he) given the descriptive words.
  arXiv  Detail & Related papers  (2024-03-01T04:47:16Z)
• Disclosure and Mitigation of Gender Bias in LLMs [64.79319733514266]
  Large Language Models (LLMs) can generate biased responses. We propose an indirect probing framework based on conditional generation. We explore three distinct strategies to disclose explicit and implicit gender bias in LLMs.
  arXiv  Detail & Related papers  (2024-02-17T04:48:55Z)
• Probing Explicit and Implicit Gender Bias through LLM Conditional Text Generation [64.79319733514266]
  Large Language Models (LLMs) can generate biased and toxic responses. We propose a conditional text generation mechanism without the need for predefined gender phrases and stereotypes.
  arXiv  Detail & Related papers  (2023-11-01T05:31:46Z)
• "Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in LLM-Generated Reference Letters [97.11173801187816]
  Large Language Models (LLMs) have recently emerged as an effective tool to assist individuals in writing various types of content. This paper critically examines gender biases in LLM-generated reference letters.
  arXiv  Detail & Related papers  (2023-10-13T16:12:57Z)
• Public Perceptions of Gender Bias in Large Language Models: Cases of ChatGPT and Ernie [2.1756081703276]
  We conducted a content analysis of social media discussions to gauge public perceptions of gender bias in large language models. People shared both observations of gender bias in their personal use and scientific findings about gender bias in LLMs. We propose governance recommendations to regulate gender bias in LLMs.
  arXiv  Detail & Related papers  (2023-09-17T00:53:34Z)
• Gender bias and stereotypes in Large Language Models [0.6882042556551611]
  This paper investigates Large Language Models' behavior with respect to gender stereotypes. We use a simple paradigm to test the presence of gender bias, building on but differing from WinoBias. Our contributions in this paper are as follows: (a) LLMs are 3-6 times more likely to choose an occupation that stereotypically aligns with a person's gender; (b) these choices align with people's perceptions better than with the ground truth as reflected in official job statistics; (d) LLMs ignore crucial ambiguities in sentence structure 95% of the time in our study items, but when explicitly prompted, they recognize
  arXiv  Detail & Related papers  (2023-08-28T22:32:05Z)
• "I'm fully who I am": Towards Centering Transgender and Non-Binary Voices to Measure Biases in Open Language Generation [69.25368160338043]
  Transgender and non-binary (TGNB) individuals disproportionately experience discrimination and exclusion from daily life. We assess how the social reality surrounding experienced marginalization of TGNB persons contributes to and persists within Open Language Generation. We introduce TANGO, a dataset of template-based real-world text curated from a TGNB-oriented community.
  arXiv  Detail & Related papers  (2023-05-17T04:21:45Z)

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.