--- title: "Who cites Scott A. Hale" description: "354 papers of the Elamur Research library cite the works of Scott A. Hale, found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/authors/scott-a-hale/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/scott-a-hale/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Scott A. Hale](https://md.research.elamur.ai/free/en/authors/scott-a-hale/) / cited by # Who cites Scott A. Hale 370 citations of their works from 354 papers of our library (of the 140 884 whose bibliographies we read) · 19 with a page here, the most cited first · [← Scott A. Hale](https://md.research.elamur.ai/free/en/authors/scott-a-hale/) - [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/deepseek-2025-ai-deepseek-r1-incentivizing-reasoning-capability/) — DeepSeek-AI, Daya Guo, Dejian Yang, et al. · 2025 · 4 593 citations - [Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models](https://md.research.elamur.ai/free/en/papers/liu-2023-summary-chatgpt-related-research-perspective/) — Yiheng Liu, Tianle Han, Siyuan Ma, et al. · 2023 · 936 citations - [Social Biases in NLP Models as Barriers for Persons with Disabilities](https://md.research.elamur.ai/free/en/papers/hutchinson-2020-social-biases-nlp-models-as/) — Ben Hutchinson, Vinodkumar Prabhakaran, Emily Denton, et al. · 2020 · 402 citations - [Tool Learning with Foundation Models](https://md.research.elamur.ai/free/en/papers/qin-2023-tool-learning-foundation-models/) — Yujia Qin, Shengding Hu, Yankai Lin, et al. · 2023 · 376 citations - [Improving alignment of dialogue agents via targeted human judgements](https://md.research.elamur.ai/free/en/papers/glaese-2022-improving-alignment-dialogue-agents-targeted/) — Amelia Glaese, Nat McAleese, Maja Trębacz, et al. · 2022 · 346 citations - [Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection](https://md.research.elamur.ai/free/en/papers/vidgen-2021-learning-worst-dynamically-generated-datasets/) — Bertie Vidgen, Tristan Thrush, Zeerak Waseem, et al. · 2021 · 320 citations - [A Holistic Approach to Undesired Content Detection in the Real World](https://md.research.elamur.ai/free/en/papers/markov-2022-holistic-approach-undesired-content-detection/) — Todor Markov, Chong Zhang, Sandhini Agarwal, et al. · 2022 · 254 citations - [LaMP: When Large Language Models Meet Personalization](https://md.research.elamur.ai/free/en/papers/salemi-2024-lamp-when-large-language-models/) — Alireza Salemi, Sheshera Mysore, Michael Bendersky, et al. · 2024 · 239 citations - [XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models](https://md.research.elamur.ai/free/en/papers/rottger-2023-xstest-test-suite-identifying-exaggerated/) — Paul Rottger, Hannah Rose Kirk, Bertie Vidgen, et al. · 2023 · 227 citations - [TrustLLM: Trustworthiness in Large Language Models](https://md.research.elamur.ai/free/en/papers/huang-2024-trustllm-trustworthiness-large-language-models/) — Yue Huang, Lichao Sun, Haoran Wang, et al. · 2024 · 226 citations - [HateCheck: Functional Tests for Hate Speech Detection Models](https://md.research.elamur.ai/free/en/papers/rottger-2021-hatecheck-functional-tests-hate-speech/) — Paul Röttger, Bertie Vidgen, Dong Nguyen, et al. · 2021 · 202 citations - [SemEval-2023 Task 10: Explainable Detection of Online Sexism](https://md.research.elamur.ai/free/en/papers/kirk-2023-semeval-2023-task-10-explainable/) — Hannah Kirk, Wenjie Yin, Bertie Vidgen, et al. · 2023 · 185 citations - [Long-form factuality in large language models](https://md.research.elamur.ai/free/en/papers/wei-2024-long-form-factuality-large-language/) — Jerry Wei, Chengrun Yang, Xinying Song, et al. · 2024 · 162 citations - [I Feel Offended, Don't Be Abusive! Implicit/Explicit Messages in Offensive and Abusive Language](https://md.research.elamur.ai/free/en/papers/caselli-2020-i-feel-offended-don-t/) — Tommaso Caselli, Valerio Basile, Jelena Mitrović, et al. · 2020 · 157 citations - [Toxic, Hateful, Offensive or Abusive? What Are We Really Classifying? An Empirical Analysis of Hate Speech Datasets](https://md.research.elamur.ai/free/en/papers/fortuna-2020-toxic-hateful-offensive-or-abusive/) — Paula Fortuna, Juan Soler, Leo Wanner · 2020 · 156 citations - [WildGuard: Open One-Stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMs](https://md.research.elamur.ai/free/en/papers/han-2024-wildguard-open-one-stop-moderation/) — Seungju Han, Kavel Rao, Allyson Ettinger, et al. · 2024 · 155 citations - [ExpertQA: Expert-Curated Questions and Attributed Answers](https://md.research.elamur.ai/free/en/papers/malaviya-2024-expertqa-expert-curated-questions-attributed/) — Chaitanya Malaviya, Subin Lee, Sihao Chen, et al. · 2024 · 109 citations - [Offensive Language and Hate Speech Detection for Danish](https://md.research.elamur.ai/free/en/papers/sigurbergsson-2020-offensive-language-hate-speech-detection/) — Gudbjartur Ingi Sigurbergsson, Leon Derczynski · 2020 · 106 citations - [TryggLLM: A Benchmark for Evaluating LLM Safety in Norwegian](https://md.research.elamur.ai/free/en/papers/touileb-2026-tryggllm-benchmark-evaluating-llm-safety/) — Samia Touileb, Truls Pedersen, Isabell Stinessen Haugen · 2026 335 more citing papers of our library have no page here yet.