🧑‍🔬 Who cites Xiaoyuan Liu

163 citations of their works from 163 papers of our library (of the 149,439 whose bibliographies we read) · 24 with a page here, the most cited first · ← Xiaoyuan Liu

1. Language Models are Few-Shot LearnersarXiv:2005.14165Tom B. Brown, Benjamin Mann, Nick Ryder, et al.2020 · 14,178 citations
2. Learning Transferable Visual Models From Natural Language SupervisionarXiv:2103.00020Alec Radford, Jong Wook Kim, Chris Hallacy, et al.2021 · 8,767 citations
3. On the Opportunities and Risks of Foundation ModelsarXiv:2108.07258Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.2021 · 3,160 citations
4. Robust Speech Recognition via Large-Scale Weak SupervisionarXiv:2212.04356Alec Radford, Jong Wook Kim, Tao Xu, et al.2022 · 2,546 citations
5. Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A SurveyACM Computing SurveysBonan Min, Hayley Ross, Elior Sulem, et al.2021 · 1,556 citations
6. Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language ModelarXiv:2201.11990Shaden Smith, Mostofa Patwary, Brandon Norick, et al.2022 · 615 citations
7. Neural Unsupervised Domain Adaptation in NLP–-A SurveyInternational Conference on Computational Linguistics (COLING)Alan Ramponi, Barbara Plank2020 · 336 citations
8. DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT ModelsConference on Neural Information Processing Systems (NeurIPS)Boxin Wang, Weixin Chen, Hengzhi Pei, et al.2023 · 259 citations
9. Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' AlignmentarXiv:2308.05374Yang Liu; Yuanshun Yao; Jean-Francois Ton; Xiaoying Zhang; Ruocheng Guo; Hao Cheng; Yegor Klochkov; Muhammad Faaiz Taufiq;, Hang Li2023 · 223 citations
10. Prompting GPT-3 To Be ReliablearXiv:2210.09150Chenglei Si, Zhe Gan, Zhengyuan Yang, et al.2022 · 210 citations
11. LILA: A Unified Benchmark for Mathematical ReasoningConference on Empirical Methods in Natural Language Processing (EMNLP)Swaroop Mishra, Matthew Finlayson, Pan Lu, et al.2022 · 179 citations
12. Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak SupervisionarXiv:2312.09390Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, et al.2023 · 172 citations
13. Context-faithful Prompting for Large Language ModelsFindings of the Association for Computational LinguisticsWenxuan Zhou, Sheng Zhang, Hoifung Poon, et al.2023 · 148 citations
14. Measure and Improve Robustness in NLP Models: A SurveyConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Xuezhi Wang, Haohan Wang, Diyi Yang2022 · 148 citations
15. Pretraining Language Models with Human PreferencesarXiv:2302.08582Tomasz Korbak, Kejian Shi, Angelica Chen, et al.2023 · 121 citations
16. Large Language Models as General Pattern MachinesarXiv:2307.04721Suvir Mirchandani, Fei Xia, Pete Florence, et al.2023 · 88 citations
17. Out-of-Distribution Detection and Selective Generation for Conditional Language ModelsarXiv:2209.15558Jie Ren, Jiaming Luo, Yao Zhao, et al.2022 · 80 citations
18. NaturalProofs: Mathematical Theorem Proving in Natural LanguagearXiv:2104.01112Sean Welleck, Jiacheng Liu, Ronan Le Bras, et al.2021 · 76 citations
19. Mind the instructions: a holistic evaluation of consistency and interactions in prompt-based learningConference on Computational Natural Language Learning (CoNLL)Lucas Weber, Elia Bruni, Dieuwke Hupkes2023 · 29 citations
20. Uncertainty Distillation: Teaching Language Models to Express Semantic ConfidencearXiv:2503.14749Sophia Hager; David Mueller; Kevin Duh;, Nicholas Andrews2025 · 5 citations
21. Task Matters: Knowledge Requirements Shape LLM Responses to Context–Memory ConflictFindings of the Association for Computational LinguisticsKaiser Sun, Fan Bai, Mark Dredze2026 · 3 citations
22. STEP3-VL-10B Technical ReportarXiv:2601.09668Ailin Huang, Chengyuan Yao, Chunrui Han, et al.2026 · 2 citations
23. When Seeing Overrides Knowing: Disentangling Knowledge Conflicts in Vision-Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Francesco Ortu, Zhijing Jin, Diego Doimo, et al.2026 · 2 citations
24. Measuring Distribution Shift in User Prompts and Its Effects on LLM PerformanceAnnual Meeting of the Association for Computational Linguistics (ACL)Parker Seegmiller, Sarah Masud Preum2026

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

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