---
title: "Who cites Sang Michael Xie — page 2 — 1,218 citing papers"
description: "1,218 papers of the Elamur Research library cite the works of Sang Michael Xie, found in their bibliographies · page 2 of 2 · 101–141; the…"
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# Who cites Sang Michael Xie

1,239 citations of their works from 1,218 papers of our library (of the 141,840 whose bibliographies we read) · 141 with a page here, the most cited first · page 2 of 2 · 101–141 · [← Sang Michael Xie](https://md.research.elamur.ai/free/en/authors/sang-michael-xie/)

[← prev](https://md.research.elamur.ai/free/en/authors/sang-michael-xie/cited-by/) [1](https://md.research.elamur.ai/free/en/authors/sang-michael-xie/cited-by/) **2**

101. [**Secrets of RLHF in Large Language Models Part I: PPO arXiv:2307.04964**Rui Zheng, Shihan Dou, Songyang Gao, et al.*2023 · 130 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2023-secrets-rlhf-large-language-models/)
102. [**InternVideo: General Video Foundation Models via Generative and Discriminative Learning arXiv:2212.03191**Yi Wang, Kunchang Li, Yizhuo Li, et al.*2022 · 128 citations*](https://md.research.elamur.ai/free/en/papers/wang-2022-internvideo-general-video-foundation/)
103. **[InfLLM: Training-Free Long-Context Extrapolation for LLMs with an Efficient Context Memory](https://md.research.elamur.ai/free/en/papers/xiao-2024-infllm-training-free-long-context/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Chaojun Xiao, Pengle Zhang, Xu Han, et al.*2024 · 125 citations*
104. [**Safety-Tuned LLaMAs: Lessons From Improving the Safety of Large Language Models that Follow Instructions arXiv:2309.07875**Federico Bianchi, Mirac Suzgun, Giuseppe Attanasio, et al.*2023 · 124 citations*](https://md.research.elamur.ai/free/en/papers/bianchi-2023-safety-tuned-llamas-lessons-improving/)
105. **[Complementary Explanations for Effective In-Context Learning](https://md.research.elamur.ai/free/en/papers/ye-2023-complementary-explanations-effective-context-learning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xi Ye, Srinivasan Iyer, Asli Celikyilmaz, et al.*2023 · 105 citations*
106. **[Learning to Retrieve In-Context Examples for Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2024-learning-retrieve-context-examples-large/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Liang Wang, Nan Yang, Furu Wei*2024 · 103 citations*
107. **[What In-Context Learning “Learns” In-Context: Disentangling Task Recognition and Task Learning](https://md.research.elamur.ai/free/en/papers/pan-2023-what-context-learning-learns-context/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jane Pan, Tianyu Gao, Howard Chen, et al.*2023 · 100 citations*
108. [**Inverse Scaling: When Bigger Isn't Better arXiv:2306.09479 · Transactions on Machine Learning Research (TMLR), 10/2023, https://openreview.net/forum?id=DwgRm72GQF**Ian R. McKenzie, Alexander Lyzhov, Michael Pieler, et al.*2023 · 99 citations*](https://md.research.elamur.ai/free/en/papers/mckenzie-2023-inverse-scaling-bigger-isn-t/)
109. [**FineWeb2: One Pipeline to Scale Them All — Adapting Pre-Training Data Processing to Every Language arXiv:2506.20920**Guilherme Penedo, Hynek Kydliček, Vinko Sabolčec, et al.*2025 · 97 citations*](https://md.research.elamur.ai/free/en/papers/penedo-2025-fineweb2-one-pipeline-scale-them/)
110. **[WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences](https://md.research.elamur.ai/free/en/papers/liu-2023-webglm-efficient-web-enhanced/) [ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)](https://md.research.elamur.ai/free/en/venues/acm-sigkdd-conference-on-knowledge-discovery-and-data-mining-kdd/)**Xiao Liu, Hanyu Lai, Hao Yu, et al.*2023 · 95 citations*
111. **[On Learning to Summarize with Large Language Models as References](https://md.research.elamur.ai/free/en/papers/liu-2024-learning-summarize-large-language-models/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Yixin Liu, Kejian Shi, Katherine He, et al.*2024 · 94 citations*
112. **[LLMs Are Few-Shot In-Context Low-Resource Language Learners](https://md.research.elamur.ai/free/en/papers/cahyawijaya-2024-llms-are-few-shot-context/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Samuel Cahyawijaya, Holy Lovenia, Pascale Fung*2024 · 92 citations*
113. [**Language Modeling Is Compression arXiv:2309.10668**Gregoire Deletang, Anian Ruoss, Paul-Ambroise Duquenne, et al.*2023 · 89 citations*](https://md.research.elamur.ai/free/en/papers/deletang-2023-language-modeling-compression/)
114. [**TextGrad: Automatic "Differentiation" via Text arXiv:2406.07496**Mert Yuksekgonul, Federico Bianchi, Joseph Boen, et al.*2024 · 88 citations*](https://md.research.elamur.ai/free/en/papers/yuksekgonul-2024-textgrad-automatic-differentiation-text/)
115. **[SeaEval for Multilingual Foundation Models: From Cross-Lingual Alignment to Cultural Reasoning](https://md.research.elamur.ai/free/en/papers/wang-2024-seaeval-multilingual-foundation-models-cross/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Bin Wang, Zhengyuan Liu, Xin Huang, et al.*2024 · 85 citations*
116. **[Complex Claim Verification with Evidence Retrieved in the Wild](https://md.research.elamur.ai/free/en/papers/chen-2024-complex-claim-verification-evidence-retrieved/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Jifan Chen, Grace Kim, Aniruddh Sriram, et al.*2024 · 84 citations*
117. **[Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation](https://md.research.elamur.ai/free/en/papers/fernandes-2023-bridging-gap-survey-integrating-human/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Patrick Fernandes, Aman Madaan, Emmy Liu, et al.*2023 · 84 citations*
118. **[Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD Examples](https://md.research.elamur.ai/free/en/papers/saparov-2023-testing-general-deductive-reasoning-capacity/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Abulhair Saparov, Richard Yuanzhe Pang, Vishakh Padmakumar, et al.*2023 · 82 citations*
119. **[Multilingual LLMs are Better Cross-lingual In-context Learners with Alignment](https://md.research.elamur.ai/free/en/papers/tanwar-2023-multilingual-llms-are-better-cross/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Eshaan Tanwar, Subhabrata Dutta, Manish Borthakur, et al.*2023 · 81 citations*
120. [**ArtPrompt: ASCII Art-based Jailbreak Attacks against Aligned LLMs arXiv:2402.11753**Fengqing Jiang, Zhangchen Xu, Luyao Niu, et al.*2024 · 80 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2024-artprompt-ascii-art-based-jailbreak/)
121. **[AudioBench: A Universal Benchmark for Audio Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2025-audiobench-universal-benchmark-audio-large/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Bin Wang, Xunlong Zou, Geyu Lin, et al.*2025 · 75 citations*
122. [**Large Language Model Routing with Benchmark Datasets arXiv:2309.15789**Tal Shnitzer, Anthony Ou, Mirian Silva, et al.*2023 · 61 citations*](https://md.research.elamur.ai/free/en/papers/shnitzer-2023-large-language-model-routing-benchmark/)
123. [**Large Language Model Agent: A Survey on Methodology, Applications and Challenges arXiv:2503.21460**Junyu Luo, Weizhi Zhang, Ye Yuan, et al.*2025 · 59 citations*](https://md.research.elamur.ai/free/en/papers/luo-2025-large-language-model-agent-survey/)
124. **[OG-RAG: Ontology-grounded retrieval-augmented generation for large language models](https://md.research.elamur.ai/free/en/papers/sharma-2025-og-rag-ontology-grounded-retrieval/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Kartik Sharma, Peeyush Kumar, Yunqing Li*2025 · 37 citations*
125. **[Pragmatics in Language Grounding: Phenomena, Tasks, and Modeling Approaches](https://md.research.elamur.ai/free/en/papers/fried-2023-pragmatics-language-grounding-phenomena-tasks/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Daniel Fried, Nicholas Tomlin, Jennifer Hu, et al.*2023 · 36 citations*
126. [**Taqyim: Evaluating Arabic NLP Tasks Using ChatGPT Models arXiv:2306.16322**Zaid Alyafeai, Maged S. Alshaibani, Badr AlKhamissi, et al.*2023 · 25 citations*](https://md.research.elamur.ai/free/en/papers/alyafeai-2023-taqyim-evaluating-arabic-nlp-tasks/)
127. [**DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules**Yanchen Liu, William Held, Diyi Yang*2023 · 20 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-dada-dialect-adaptation-dynamic-aggregation/)
128. [**Evaluating statistical language models as pragmatic reasoners arXiv:2305.01020**Benjamin Lipkin, Lionel Wong, Gabriel Grand, et al.*2023 · 18 citations*](https://md.research.elamur.ai/free/en/papers/lipkin-2023-evaluating-statistical-language-models-pragmatic/)
129. **[Bridging the Resource Gap: Exploring the Efficacy of English and Multilingual LLMs for Swedish](https://md.research.elamur.ai/free/en/papers/holmstrom-2023-bridging-resource-gap-exploring-efficacy/) [Workshop on Resources and Representations for Under-Resourced Languages and Domains](https://md.research.elamur.ai/free/en/venues/resourceful/)**Oskar Holmström, Jenny Kunz, Marco Kuhlmann*2023 · 8 citations*
130. [**MedAraBench: Large-Scale Arabic Medical Question Answering Dataset and Benchmark arXiv:2602.01714**Mouath Abu-Daoud, Leen Kharouf, Omar El Hajj, et al.*2026 · 5 citations*](https://md.research.elamur.ai/free/en/papers/abu-daoud-2026-medarabench-large-scale-arabic-medical/)
131. **[Gender Disambiguation in Machine Translation: Diagnostic Evaluation in Decoder-Only Architectures](https://md.research.elamur.ai/free/en/papers/manna-2026-gender-disambiguation-machine-translation-diagnostic/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Chiara Manna, Hosein Mohebbi, Afra Alishahi, et al.*2026 · 3 citations*
132. **[Benchmarking Large Language Models for Chinese and Japanese IMEs: Phonetic-to-Character Generation and Textual Error Correction](https://md.research.elamur.ai/free/en/papers/zou-2026-benchmarking-large-language-models-chinese/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Yuchun Zou, Tedd Lee, Xiaodi Fan, et al.*2026 · 2 citations*
133. **[Construction Grammar Evidence for How LLMs Use Context-Directed Extrapolation to Solve Tasks](https://md.research.elamur.ai/free/en/papers/madabushi-2025-construction-grammar-evidence-how-llms/) [International Workshop on Construction Grammars and NLP](https://md.research.elamur.ai/free/en/venues/cxgsnlp/)**Harish Tayyar Madabushi, Claire Bonial*2025 · 1 citation*
134. **[AI Safety Lost in Translation: Evaluating the Effectiveness of English-Italian Cross-Lingual LLM Safety Alignment](https://md.research.elamur.ai/free/en/papers/wu-2026-ai-safety-lost-translation-evaluating/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Alessio Wu, Martim Brandao*2026*
135. **[AmazoniaNLP: A Survey of Extreme Low-Resource Languages in the Peruvian-Brazilian Amazon](https://md.research.elamur.ai/free/en/papers/zevallos-2026-amazonianlp-survey-extreme-low-resource/) [SIGUL Joint Workshop with ELE, EURALI, and DCLRL: Towards Inclusivity and Equality: Language Resources and Technologies for Under-Resourced and Endangered Languages](https://md.research.elamur.ai/free/en/venues/sigul/)**Rodolfo Joel Zevallos, Fabrício Carraro, John E. Ortega*2026*
136. **[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*
137. **[Is Biomedical Specialization Still Worth It? Insights from Domain-Adaptive Language Modelling with a New French Health Corpus](https://md.research.elamur.ai/free/en/papers/mannion-2026-is-biomedical-specialization-still-worth/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Aidan Mannion, Cécile Macaire, Armand Violle, et al.*2026*
138. [**Sentiment Classification of Gaza War Headlines: A Comparative Analysis of Large Language Models and Arabic Fine-Tuned BERT Models arXiv:2604.08566 · SSRN (2026)**Amr Eleraqi; Hager H. Mustafa;, Abdul Hadi N. Ahmed*2026*](https://md.research.elamur.ai/free/en/papers/eleraqi-2026-sentiment-classification-gaza-war-headlines/)
139. [**Embodied Intelligence Systems Based on Large Models: A Survey (基于大模型的具身智能系统综述) Acta Automatica Sinica (自动化学报)**Wensheng Wang (王文晟), Ning Tan (谭宁), Kai Huang (黄凯), et al.*2025*](https://md.research.elamur.ai/free/en/papers/wang-2025-embodied-intelligence-systems/)
140. **[Survey on Hallucinations in Large Language Models (大语言模型的幻觉问题研究综述)](https://md.research.elamur.ai/free/en/papers/liu-2024-hallucinations-large-language/) [Journal of Software](https://md.research.elamur.ai/free/en/venues/journal-of-software/)**Zeyuan Liu (刘泽垣), Pengjiang Wang (王鹏江), Xiaobin Song (宋晓斌), et al.*2024*
141. [**Questions After ChatGPT: Opportunities and Challenges of Very Large Pre-trained Models (问答ChatGPT之后: 超大预训练模型的机遇和挑战) Acta Automatica Sinica (自动化学报)**Jingwei Lu (卢经纬), Chao Guo (郭超), Xingyuan Dai (戴星原), et al.*2023*](https://md.research.elamur.ai/free/en/papers/lu-2023-questions-after-chatgpt/)

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