---
title: "Who cites “Fifty Shades of Bias”: Normative — 16 citing papers"
description: "16 papers of the Elamur Research library cite «“Fifty Shades of Bias”: Normative Ratings of Gender Bias in GPT Generated English Text»…"
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---
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# Who cites «“Fifty Shades of Bias”: Normative Ratings of Gender Bias in GPT Generated English Text»

16 papers of our library cite it (of the 149,439 whose bibliographies we read) · 5 with a page here, the most cited first · [← back to «“Fifty Shades of Bias”: Normative Ratings of Gender Bias in GPT Generated English Text»](https://md.research.elamur.ai/free/en/papers/hada-2023-fifty-shades-bias-normative-ratings/)

1. **[Beyond Prompt Brittleness: Evaluating the Reliability and Consistency of Political Worldviews in LLMs](https://md.research.elamur.ai/free/en/papers/ceron-2024-beyond-prompt-brittleness-evaluating-reliability/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Tanise Ceron, Neele Falk, Ana Barić, et al.*2024 · 46 citations*
2. **[Investigating Bias in LLM-Based Bias Detection: Disparities between LLMs and Human Perception](https://md.research.elamur.ai/free/en/papers/lin-2025-investigating-bias-llm-based-bias/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Luyang Lin, Lingzhi Wang, Jinsong Guo, et al.*2025 · 44 citations*
3. **[Sociodemographic Bias in Language Models: A Survey and Forward Path](https://md.research.elamur.ai/free/en/papers/gupta-2023-sociodemographic-bias-language-models-survey/) [Workshop on Gender Bias in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/gebnlp/)**Vipul Gupta, Pranav Narayanan Venkit, Shomir Wilson, et al.*2023 · 40 citations*
4. **[Evaluating the Effect of Retrieval Augmentation on Social Biases](https://md.research.elamur.ai/free/en/papers/zhang-2026-evaluating-effect-retrieval-augmentation-social/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Tianhui Zhang, Yi Zhou, Danushka Bollegala*2026 · 3 citations*
5. **[ArGAN: Arabic Gender, Ability, and Nationality Dataset for Evaluating Biases in Large Language Models](https://md.research.elamur.ai/free/en/papers/aly-2025-argan-arabic-gender-ability-nationality/) [Workshop on Gender Bias in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/gebnlp/)**Ranwa Aly, Yara Allam, Rana Gaber, et al.*2025 · 1 citation*

11 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/hada-2023-fifty-shades-bias-normative-ratings/) — “Fifty Shades of Bias”: Normative Ratings of Gender Bias in GPT Generated English Text