---
title: "Who cites Noe Casas"
description: "88 papers of the Elamur Research library cite the works of Noe Casas, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/noe-casas/cited-by/"
---
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# Who cites Noe Casas

89 citations of their works from 88 papers of our library (of the 140 884 whose bibliographies we read) · 13 with a page here, the most cited first · [← Noe Casas](https://md.research.elamur.ai/free/en/authors/noe-casas/)

- [On the Opportunities and Risks of Foundation Models](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/) — Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al. · 2021 · 3 044 citations
- [Language (Technology) is Power: A Critical Survey of “Bias” in NLP](https://md.research.elamur.ai/free/en/papers/blodgett-2020-language-technology-is-power-critical/) — Su Lin Blodgett, Solon Barocas, Hal Daumé III, et al. · 2020 · 1 700 citations
- [LaMDA: Language Models for Dialog Applications](https://md.research.elamur.ai/free/en/papers/thoppilan-2022-lamda-language-models-dialog-applications/) — Romal Thoppilan, Daniel De Freitas, Jamie Hall, et al. · 2022 · 1 314 citations
- [RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models](https://md.research.elamur.ai/free/en/papers/gehman-2020-realtoxicityprompts-evaluating-neural-toxic-degeneration/) — Samuel Gehman, Suchin Gururangan, Maarten Sap, et al. · 2020 · 1 277 citations
- [Measuring Bias in Contextualized Word Representations](https://md.research.elamur.ai/free/en/papers/kurita-2019-measuring-bias-contextualized-word-representations/) — Keita Kurita, Nidhi Vyas, Ayush Pareek, et al. · 2019 · 584 citations
- [Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned](https://md.research.elamur.ai/free/en/papers/ganguli-2022-red-teaming-language-models-reduce/) — Deep Ganguli, Liane Lovitt, Jackson Kernion, et al. · 2022 · 456 citations
- [The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale](https://md.research.elamur.ai/free/en/papers/penedo-2024-fineweb-datasets-decanting-web-finest/) — Guilherme Penedo, Hynek Kydlíček, Loubna Ben allal, et al. · 2024 · 404 citations
- [Toxicity in chatgpt: Analyzing persona-assigned language models](https://md.research.elamur.ai/free/en/papers/deshpande-2023-toxicity-chatgpt-analyzing-persona-assigned/) — Ameet Deshpande, Vishvak Murahari, Tanmay Rajpurohit, et al. · 2023 · 344 citations
- [Towards Debiasing Sentence Representations](https://md.research.elamur.ai/free/en/papers/liang-2020-debiasing-sentence-representations/) — Paul Pu Liang, Irene Mengze Li, Emily Zheng, et al. · 2020 · 259 citations
- [Measuring and Reducing Gendered Correlations in Pre-trained Models](https://md.research.elamur.ai/free/en/papers/webster-2020-measuring-reducing-gendered-correlations-pre/) — Kellie Webster, Xuezhi Wang, Ian Tenney, et al. · 2020 · 252 citations
- [Interpreting Pretrained Contextualized Representations via Reductions to Static Embeddings](https://md.research.elamur.ai/free/en/papers/bommasani-2020-interpreting-pretrained-contextualized-representations-reductions/) — Rishi Bommasani, Kelly Davis, Claire Cardie · 2020 · 248 citations
- [Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP](https://md.research.elamur.ai/free/en/papers/schick-2021-self-diagnosis-self-debiasing-proposal/) — Timo Schick, Sahana Udupa, Hinrich Schutze · 2021 · 207 citations
- [Investigating How LLMs Propagate Female Stereotypes: Comparing What Models Say via Prompts with What They Represent in Their Embeddings](https://md.research.elamur.ai/free/en/papers/nunez-2026-investigating-how-llms-propagate-female/) — Andrea Valderrey Nuñez, Jelke Bloem · 2026

75 more citing papers of our library have no page here yet.
