---
title: "Who cites Alexandra Olteanu — 211 papers of our library"
description: "211 papers of the Elamur Research library cite the works of Alexandra Olteanu, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/alexandra-olteanu/cited-by/"
---
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# Who cites Alexandra Olteanu

213 citations of their works from 211 papers of our library (of the 149,439 whose bibliographies we read) · 46 with a page here, the most cited first · [← Alexandra Olteanu](https://md.research.elamur.ai/free/en/authors/alexandra-olteanu/)

1. [**Llama 2: Open Foundation and Fine-Tuned Chat Models arXiv:2307.09288**Hugo Touvron, Louis Martin, Kevin Stone, et al.*2023 · 9,669 citations*](https://md.research.elamur.ai/free/en/papers/touvron-2023-llama-2-open-foundation-fine/)
2. [**PaLM: Scaling Language Modeling with Pathways arXiv:2204.02311**Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, et al.*2022 · 4,753 citations*](https://md.research.elamur.ai/free/en/papers/chowdhery-2022-palm-scaling-language-modeling-pathways/)
3. [**On the Opportunities and Risks of Foundation Models arXiv:2108.07258**Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.*2021 · 3,160 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/)
4. [**Gemini: A Family of Highly Capable Multimodal Models arXiv:2312.11805**Gemini Team, Rohan Anil, Sebastian Borgeaud, et al.*2023 · 3,050 citations*](https://md.research.elamur.ai/free/en/papers/gemini-team-2023-gemini-family-highly-capable-multimodal/)
5. [**OPT: Open Pre-trained Transformer Language Models arXiv:2205.01068**Susan Zhang, Stephen Roller, Naman Goyal, et al.*2022 · 1,755 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2022-opt-open-pre-trained-transformer/)
6. [**CTRL: A Conditional Transformer Language Model for Controllable Generation arXiv:1909.05858**Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, et al.*2019 · 1,291 citations*](https://md.research.elamur.ai/free/en/papers/keskar-2019-ctrl-conditional-transformer-language-model/)
7. **[Bias and Fairness in Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/gallegos-2023-bias-fairness-large-language-models/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow, et al.*2023 · 1,082 citations*
8. [**PaLM 2 Technical Report arXiv:2305.10403**Rohan Anil, Andrew M. Dai, Orhan Firat, et al.*2023 · 1,000 citations*](https://md.research.elamur.ai/free/en/papers/anil-2023-palm-2-technical-report/)
9. [**GLM-130B: An Open Bilingual Pre-trained Model arXiv:2210.02414**Aohan Zeng, Xiao Liu, Zhengxiao Du, et al.*2022 · 786 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2022-glm-130b-open-bilingual-pre/)
10. **[BBQ: A hand-built bias benchmark for question answering](https://md.research.elamur.ai/free/en/papers/parrish-2022-bbq-hand-built-bias-benchmark/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Alicia Parrish, Angelica Chen, Nikita Nangia, et al.*2022 · 497 citations*
11. [**GLaM: Efficient Scaling of Language Models with Mixture-of-Experts arXiv:2112.06905**Nan Du, Yanping Huang, Andrew M. Dai, et al.*2021 · 496 citations*](https://md.research.elamur.ai/free/en/papers/du-2021-glam-efficient-scaling-language-models/)
12. [**Ethical and social risks of harm from Language Models arXiv:2112.04359**Laura Weidinger, John Mellor, Maribeth Rauh, et al.*2021 · 456 citations*](https://md.research.elamur.ai/free/en/papers/weidinger-2021-ethical-social-risks-harm-language/)
13. [**Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims arXiv:2004.07213**Miles Brundage, Shahar Avin, Jasmine Wang, et al.*2020 · 320 citations*](https://md.research.elamur.ai/free/en/papers/brundage-2020-trustworthy-ai-development-mechanisms/)
14. **[DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models](https://md.research.elamur.ai/free/en/papers/wang-2023-decodingtrust-comprehensive-assessment-trustworthiness-gpt/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Boxin Wang, Weixin Chen, Hengzhi Pei, et al.*2023 · 259 citations*
15. **[Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models](https://md.research.elamur.ai/free/en/papers/cheng-2023-marked-personas-natural-language-prompts/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Myra Cheng, Esin Durmus, Dan Jurafsky*2023 · 246 citations*
16. **[An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models](https://md.research.elamur.ai/free/en/papers/meade-2022-empirical-survey-effectiveness-debiasing-techniques/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Nicholas Meade, Elinor Poole-Dayan, Siva Reddy*2022 · 211 citations*
17. **[On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning](https://md.research.elamur.ai/free/en/papers/shaikh-2023-second-thought-let-s-not/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Omar Shaikh, Hongxin Zhang, William Held, et al.*2023 · 192 citations*
18. [**Evaluating Large Language Models: A Comprehensive Survey arXiv:2310.19736**Zishan Guo, Renren Jin, Chuang Liu, et al.*2023 · 182 citations*](https://md.research.elamur.ai/free/en/papers/guo-2023-evaluating-large-language-models-comprehensive/)
19. **[“I'm sorry to hear that”: Finding New Biases in Language Models with a Holistic Descriptor Dataset](https://md.research.elamur.ai/free/en/papers/smith-2022-i-m-sorry-hear-that/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Eric Michael Smith, Melissa Hall, Melanie Kambadur, et al.*2022 · 167 citations*
20. **[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/) [BigScience Episode – Workshop on Challenges & Perspectives in Creating Large Language Models](https://md.research.elamur.ai/free/en/venues/bigscience/)**Zeerak Talat, Aurélie Névéol, Stella Biderman, et al.*2022 · 130 citations*
21. [**Efficient Large Scale Language Modeling with Mixtures of Experts arXiv:2112.10684**Mikel Artetxe, Shruti Bhosale, Naman Goyal, et al.*2021 · 113 citations*](https://md.research.elamur.ai/free/en/papers/artetxe-2021-efficient-large-scale-language-modeling/)
22. **[French CrowS-Pairs: Extending a challenge dataset for measuring social bias in masked language models to a language other than English](https://md.research.elamur.ai/free/en/papers/neveol-2022-french-crows-pairs-extending-challenge/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Aurélie Névéol, Yoann Dupont, Julien Bezançon, et al.*2022 · 107 citations*
23. **[On the Intrinsic and Extrinsic Fairness Evaluation Metrics for Contextualized Language Representations](https://md.research.elamur.ai/free/en/papers/cao-2022-intrinsic-extrinsic-fairness-evaluation-metrics/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yang Trista Cao, Yada Pruksachatkun, Kai-Wei Chang, et al.*2022 · 100 citations*
24. [**Automatically Auditing Large Language Models via Discrete Optimization arXiv:2303.04381**Erik Jones, Anca Dragan, Aditi Raghunathan, et al.*2023 · 89 citations*](https://md.research.elamur.ai/free/en/papers/jones-2023-automatically-auditing-large-language-models/)
25. **[Hate Speech Classifiers Learn Normative Social Stereotypes](https://md.research.elamur.ai/free/en/papers/davani-2023-hate-speech-classifiers-learn-normative/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Aida Mostafazadeh Davani, Mohammad Atari, Brendan Kennedy, et al.*2023 · 84 citations*
26. **[Theory-Grounded Measurement of U.S. Social Stereotypes in English Language Models](https://md.research.elamur.ai/free/en/papers/cao-2022-theory-grounded-measurement-u-s/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Yang Trista Cao, Anna Sotnikova, Hal Daumé III, et al.*2022 · 50 citations*
27. **[ANLIzing the Adversarial Natural Language Inference Dataset](https://md.research.elamur.ai/free/en/papers/williams-2022-anlizing-adversarial-natural-language-inference/) [Society for Computation in Linguistics (SCiL)](https://md.research.elamur.ai/free/en/venues/scil/)**Adina Williams, Tristan Thrush, Douwe Kiela*2022 · 48 citations*
28. **[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*
29. **[“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/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Rishav Hada, Agrima Seth, Harshita Diddee, et al.*2023 · 27 citations*
30. [**An Empirical Study of Metrics to Measure Representational Harms in Pre-Trained Language Models arXiv:2301.09211**Saghar Hosseini, Hamid Palangi, Ahmed Hassan Awadallah*2023 · 19 citations*](https://md.research.elamur.ai/free/en/papers/hosseini-2023-empirical-study-metrics-measure-representational/)
31. **[Identifying and Adapting Transformer-Components Responsible for Gender Bias in an English Language Model](https://md.research.elamur.ai/free/en/papers/chintam-2023-identifying-adapting-transformer-components-responsible/) [BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP](https://md.research.elamur.ai/free/en/venues/blackboxnlp/)**Abhijith Chintam, Rahel Beloch, Willem Zuidema, et al.*2023 · 19 citations*
32. **[CAW-coref: Conjunction-Aware Word-level Coreference Resolution](https://md.research.elamur.ai/free/en/papers/oosterlinck-2023-caw-coref-conjunction-aware-word/) [Workshop on Computational Models of Reference, Anaphora and Coreference (CRAC)](https://md.research.elamur.ai/free/en/venues/crac/)**Karel D'Oosterlinck, Semere Kiros Bitew, Brandon Papineau, et al.*2023 · 17 citations*
33. **[Uncertainty and Inclusivity in Gender Bias Annotation: An Annotation Taxonomy and Annotated Datasets of British English Text](https://md.research.elamur.ai/free/en/papers/havens-2022-uncertainty-inclusivity-gender-bias-annotation/) [Workshop on Gender Bias in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/gebnlp/)**Lucy Havens, Melissa Terras, Benjamin Bach, et al.*2022 · 14 citations*
34. [**Bridging Context Gaps: Leveraging Coreference Resolution for Long Contextual Understanding arXiv:2410.01671**Yanming Liu, Xinyue Peng, Jiannan Cao, et al.*2024 · 10 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-bridging-context-gaps-leveraging-coreference/)
35. [**Challenges to Evaluating the Generalization of Coreference Resolution Models: A Measurement Modeling Perspective**Ian Porada, Alexandra Olteanu, Kaheer Suleman, et al.*2024 · 10 citations*](https://md.research.elamur.ai/free/en/papers/porada-2024-challenges-evaluating-generalization-coreference-resolution/)
36. [**"Im not Racist but...": Discovering Bias in the Internal Knowledge of Large Language Models arXiv:2310.08780**Abel Salinas, Louis Penafiel, Robert McCormack, et al.*2023 · 10 citations*](https://md.research.elamur.ai/free/en/papers/salinas-2023-im-racist-but-discovering-bias/)
37. [**Debiasing Algorithm through Model Adaptation arXiv:2310.18913**Tomasz Limisiewicz, David Mareček, Tomaš Musil*2023 · 10 citations*](https://md.research.elamur.ai/free/en/papers/limisiewicz-2023-debiasing-algorithm-through-model-adaptation/)
38. **[A Controlled Reevaluation of Coreference Resolution Models](https://md.research.elamur.ai/free/en/papers/porada-2024-controlled-reevaluation-coreference-resolution-models/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Ian Porada, Xiyuan Zou, Jackie Chi Kit Cheung*2024 · 7 citations*
39. [**Exploring the Jungle of Bias: Political Bias Attribution in Language Models via Dependency Analysis arXiv:2311.08605**David F. Jenny, Yann Billeter, Mrinmaya Sachan, et al.*2023 · 6 citations*](https://md.research.elamur.ai/free/en/papers/jenny-2023-exploring-jungle-bias-political-bias/)
40. **[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*
41. **[GKnow: Measuring the Entanglement of Gender Bias and Factual Gender](https://md.research.elamur.ai/free/en/papers/veloso-2026-gknow-measuring-entanglement-gender-bias/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Leonor Veloso, Hinrich Schuetze*2026*
42. **[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/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Andrea Valderrey Nuñez, Jelke Bloem*2026*
43. **[Practising responsibility: Ethics in NLP as a hands-on course](https://md.research.elamur.ai/free/en/papers/nissim-2026-practising-responsibility-ethics-nlp-as/) [Workshop on Teaching Natural Language Processing](https://md.research.elamur.ai/free/en/venues/teachingnlp/)**Malvina Nissim, Viviana Patti, Beatrice Savoldi*2026*
44. **[Quantifying Metric and Model Agreement in Bias Evaluation of Large Language Models](https://md.research.elamur.ai/free/en/papers/asgari-2026-quantifying-metric-model-agreement-bias/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Arash Asgari, Huan Wu, Amirreza Naziri, et al.*2026*
45. **[Rethinking Research on Stereotypes: An Analysis through Social Psychological and Computational Perspectives](https://md.research.elamur.ai/free/en/papers/shejole-2026-rethinking-research-stereotypes-analysis-through/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kaustubh Shivshankar Shejole, Pushpak Bhattacharyya*2026*
46. [**The Validity of Coreference-based Evaluations of Natural Language Understanding arXiv:2602.16200**Ian Porada*2026*](https://md.research.elamur.ai/free/en/papers/porada-2026-validity-coreference-based-evaluations-natural/)

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