---
title: "Who cites UNQOVERing Stereotyping Biases via…"
description: "87 papers of the Elamur Research library cite «UNQOVERing Stereotyping Biases via Underspecified Questions», found in their bibliographies; the most…"
canonical: "https://research.elamur.ai/free/en/papers/li-2020-unqovering-stereotyping-biases-underspecified-questions/cited-by/"
---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [UNQOVERing Stereotyping Biases via Underspecified…](https://md.research.elamur.ai/free/en/papers/li-2020-unqovering-stereotyping-biases-underspecified-questions/) / cited by

# Who cites «UNQOVERing Stereotyping Biases via Underspecified Questions»

87 papers of our library cite it (of the 140884 whose bibliographies we read) · 9 with a page here, the most cited first · [← the paper](https://md.research.elamur.ai/free/en/papers/li-2020-unqovering-stereotyping-biases-underspecified-questions/)

- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/) — Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al. · 2022 · 1 695 citations
- [Bias and Fairness in Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/gallegos-2023-bias-fairness-large-language-models/) — Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow, et al. · 2023 · 1 072 citations
- [GLaM: Efficient Scaling of Language Models with Mixture-of-Experts](https://md.research.elamur.ai/free/en/papers/du-2021-glam-efficient-scaling-language-models/) — Nan Du, Yanping Huang, Andrew M. Dai, et al. · 2021 · 474 citations
- [From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models](https://md.research.elamur.ai/free/en/papers/feng-2023-pretraining-data-language-models-downstream/) — Shangbin Feng, Chan Young Park, Yuhan Liu, et al. · 2023 · 305 citations
- [DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models](https://md.research.elamur.ai/free/en/papers/wang-2023-decodingtrust-comprehensive-assessment-trustworthiness-gpt/) — Boxin Wang, Weixin Chen, Hengzhi Pei, et al. · 2023 · 246 citations
- [NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark](https://md.research.elamur.ai/free/en/papers/sainz-2023-nlp-evaluation-trouble-need-measure/) — Oscar Sainz, Jon Campos, Iker García-Ferrero, et al. · 2023 · 245 citations
- [Evaluating Large Language Models: A Comprehensive Survey](https://md.research.elamur.ai/free/en/papers/guo-2023-evaluating-large-language-models-comprehensive/) — Zishan Guo, Renren Jin, Chuang Liu, et al. · 2023 · 177 citations
- [Large Language Model Alignment: A Survey](https://md.research.elamur.ai/free/en/papers/shen-2023-large-language-model-alignment-survey/) — Tianhao Shen, Renren Jin, Yufei Huang, et al. · 2023 · 123 citations
- [Are Social Biases in LLMs Consistent across Generative Tasks? A Case Study for Basque](https://md.research.elamur.ai/free/en/papers/zulaika-2026-are-social-biases-llms-consistent/) — Muitze Zulaika, Xabier Saralegi, Julia Shershneva, et al. · 2026

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