--- title: "Who cites You reap what you sow: On the — 71 citing papers" description: "71 papers of the Elamur Research library cite «You reap what you sow: On the Challenges of Bias Evaluation Under Multilingual Settings»…" canonical: "https://research.elamur.ai/free/en/papers/talat-2022-you-reap-what-you-sow/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/talat-2022-you-reap-what-you-sow/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [You reap what you sow: On the Challenges of Bias…](https://md.research.elamur.ai/free/en/papers/talat-2022-you-reap-what-you-sow/) / cited by # Who cites «You reap what you sow: On the Challenges of Bias Evaluation Under Multilingual Settings» 71 papers of our library cite it (of the 141,840 whose bibliographies we read) · 16 with a page here, the most cited first · [← back to «You reap what you sow: On the Challenges of Bias Evaluation Under Multilingual Settings»](https://md.research.elamur.ai/free/en/papers/talat-2022-you-reap-what-you-sow/) 1. [**Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models arXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5Bvosj**Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al.*2022 · 1,703 citations*](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/) 2. [**A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly arXiv:2312.02003**Yifan Yao, Jinhao Duan, Kaidi Xu, et al.*2023 · 1,182 citations*](https://md.research.elamur.ai/free/en/papers/yao-2023-survey-large-language-model-llm/) 3. **[GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://md.research.elamur.ai/free/en/papers/black-2022-gpt-neox-20b-open-source/) [BigScience Episode – Workshop on Challenges & Perspectives in Creating Large Language Models](https://md.research.elamur.ai/free/en/venues/bigscience/)**Sidney Black, Stella Biderman, Eric Hallahan, et al.*2022 · 821 citations* 4. [**The Flan Collection: Designing Data and Methods for Effective Instruction Tuning arXiv:2301.13688**Shayne Longpre, Le Hou, Tu Vu, et al.*2023 · 592 citations*](https://md.research.elamur.ai/free/en/papers/longpre-2023-flan-collection-designing-data-methods/) 5. **[Having Beer after Prayer? Measuring Cultural Bias in Large Language Models](https://md.research.elamur.ai/free/en/papers/naous-2023-having-beer-after-prayer-measuring/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tarek Naous, Michael J. Ryan, Alan Ritter, et al.*2023 · 268 citations* 6. [**Red teaming ChatGPT via Jailbreaking: Bias, Robustness, Reliability and Toxicity arXiv:2301.12867**Terry Yue Zhuo, Yujin Huang, Chunyang Chen, et al.*2023 · 246 citations*](https://md.research.elamur.ai/free/en/papers/zhuo-2023-red-teaming-chatgpt-jailbreaking-bias/) 7. [**Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model arXiv:2402.07827**Ahmet Üstün, Viraat Aryabumi, Zheng-Xin Yong, et al.*2024 · 245 citations*](https://md.research.elamur.ai/free/en/papers/ustun-2024-aya-model-instruction-finetuned-open/) 8. **[Do All Languages Cost the Same? Tokenization in the Era of Commercial Language Models](https://md.research.elamur.ai/free/en/papers/ahia-2023-do-all-languages-cost-same/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Orevaoghene Ahia, Sachin Kumar, Hila Gonen, et al.*2023 · 164 citations* 9. [**The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset arXiv:2303.03915**Hugo Laurençon, Lucile Saulnier, Thomas Wang, et al.*2023 · 147 citations*](https://md.research.elamur.ai/free/en/papers/lauren-ccon-2023-bigscience-roots-corpus-1-6tb/) 10. **[ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic](https://md.research.elamur.ai/free/en/papers/koto-2024-arabicmmlu-assessing-massive-multitask-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Fajri Koto, Haonan Li, Sara Shatnawi, et al.*2024 · 116 citations* 11. **[BLOOM+1: Adding Language Support to BLOOM for Zero-Shot Prompting](https://md.research.elamur.ai/free/en/papers/yong-2023-bloom-1-adding-language-support/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zheng Xin Yong, Hailey Schoelkopf, Niklas Muennighoff, et al.*2023 · 101 citations* 12. **[Inseq: An Interpretability Toolkit for Sequence Generation Models](https://md.research.elamur.ai/free/en/papers/sarti-2023-inseq-interpretability-toolkit-sequence-generation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Gabriele Sarti, Nils Feldhus, Ludwig Sickert, et al.*2023 · 96 citations* 13. **[Gender Bias Evaluation in Machine Translation for Amharic, Tigrigna, and Afaan Oromoo](https://md.research.elamur.ai/free/en/papers/sewunetie-2024-gender-bias-evaluation-machine-translation/) [Workshop on Gender-Inclusive Translation Technologies](https://md.research.elamur.ai/free/en/venues/gitt/)**Walelign Sewunetie, Atnafu Tonja, Tadesse Belay, et al.*2024 · 5 citations* 14. **[Evaluating Machine Translation Datasets for Low-Web Data Languages: A Gendered Lens](https://md.research.elamur.ai/free/en/papers/nigatu-2026-evaluating-machine-translation-datasets-low/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hellina Hailu Nigatu, Bethelhem Yemane Mamo, Bontu Fufa Balcha, et al.*2026 · 1 citation* 15. **[How Far Can Bias Go? Tracing Bias from Pre-Training Data to Alignment](https://md.research.elamur.ai/free/en/papers/thaler-2026-how-far-can-bias-go/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Marion Thaler, Abdullatif Köksal, Alina Leidinger, et al.*2026* 16. **[Yeswa-Stories: A Three-Way Parallel Dataset of Female African Figures in Low-Web Data Languages](https://md.research.elamur.ai/free/en/papers/mamo-2026-yeswa-stories-three-way-parallel/) [Workshop on Gender-Inclusive Translation Technologies](https://md.research.elamur.ai/free/en/venues/gitt/)**Bethelhem Mamo, Hellina Nigatu*2026* 55 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/talat-2022-you-reap-what-you-sow/) — You reap what you sow: On the Challenges of Bias Evaluation Under Multilingual Settings