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Fairness and social bias quantification in Large Language Models for sentiment analysis
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References

  • 2023ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope2,455 cited
  • 2018Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science840 cited
  • 2021StereoSet: Measuring stereotypical bias in pretrained language models643 cited
  • 2023Gender bias and stereotypes in Large Language Models381 cited
  • 2019Gender Bias in Contextualized Word Embeddings334 cited
  • 2021Five sources of bias in natural language processing298 cited
  • 2020Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection286 cited
  • 2023Should ChatGPT be biased? Challenges and risks of bias in large language models282 cited
  • 2021Towards Understanding and Mitigating Social Biases in Language Models153 cited
  • 2020Investigating Gender Bias in Language Models Using Causal Mediation Analysis142 cited
  • 2021Bias Out-of-the-Box: An Empirical Analysis of Intersectional Occupational Biases in Popular Generative Language Models72 cited
  • 2022Quantifying and alleviating political bias in language models68 cited
  • 2021Mitigating Political Bias in Language Models through Reinforced Calibration64 cited
  • 2021Stereotype and Skew: Quantifying Gender Bias in Pre-trained and Fine-tuned Language Models

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59 cited
  • 2021A Survey on Gender Bias in Natural Language Processing57 cited
  • 2023The Unequal Opportunities of Large Language Models: Examining Demographic Biases in Job Recommendations by ChatGPT and LLaMA42 cited
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