---
title: "Who cites Tao Wang — page 2 — 932 papers of our library"
description: "932 papers of the Elamur Research library cite the works of Tao Wang, found in their bibliographies · page 2 of 2 · 101–131; the most cited first."
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---
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# Who cites Tao Wang

944 citations of their works from 932 papers of our library (of the 141,840 whose bibliographies we read) · 131 with a page here, the most cited first · page 2 of 2 · 101–131 · [← Tao Wang](https://md.research.elamur.ai/free/en/authors/tao-wang/)

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

101. [**Skywork: A More Open Bilingual Foundation Model arXiv:2310.19341**Tianwen Wei, Liang Zhao, Lichang Zhang, et al.*2023 · 91 citations*](https://md.research.elamur.ai/free/en/papers/wei-2023-skywork-more-open-bilingual/)
102. [**PromptNER: Prompting For Named Entity Recognition arXiv:2305.15444**Dhananjay Ashok, Zachary C. Lipton*2023 · 86 citations*](https://md.research.elamur.ai/free/en/papers/ashok-2023-promptner-prompting-named-entity-recognition/)
103. [**Universal Self-Consistency for Large Language Model Generation arXiv:2311.17311**Xinyun Chen, Renat Aksitov, Uri Alon, et al.*2023 · 86 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-universal-self-consistency-large-language/)
104. **[LLMs cannot find reasoning errors, but can correct them given the error location](https://md.research.elamur.ai/free/en/papers/tyen-2024-llms-cannot-find-reasoning-errors/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Gladys Tyen, Hassan Mansoor, Victor Carbune, et al.*2024 · 85 citations*
105. **[InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedback](https://md.research.elamur.ai/free/en/papers/yang-2023-intercode-standardizing-benchmarking-interactive-coding/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**John Yang, Akshara Prabhakar, Karthik Narasimhan, et al.*2023 · 83 citations*
106. **[PKU-SafeRLHF: Towards Multi-Level Safety Alignment for LLMs with Human Preference](https://md.research.elamur.ai/free/en/papers/ji-2025-pku-saferlhf-multi-level-safety/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jiaming Ji, Donghai Hong, Borong Zhang, et al.*2025 · 81 citations*
107. **[Benchmarking Large Language Models on Answering and Explaining Challenging Medical Questions](https://md.research.elamur.ai/free/en/papers/chen-2025-benchmarking-large-language-models-answering/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Hanjie Chen, Zhouxiang Fang, Yash Singla, et al.*2025 · 75 citations*
108. [**Aligning with Human Judgement: The Role of Pairwise Preference in Large Language Model Evaluators arXiv:2403.16950**Yinhong Liu, Han Zhou, Zhijiang Guo, et al.*2024 · 75 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-aligning-human-judgement-role-pairwise/)
109. [**SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling arXiv:2312.15166**Dahyun Kim, Chanjun Park, Sanghoon Kim, et al.*2023 · 75 citations*](https://md.research.elamur.ai/free/en/papers/kim-2023-solar-10-7b-scaling-large/)
110. **[MEGAVERSE: Benchmarking Large Language Models Across Languages, Modalities, Models and Tasks](https://md.research.elamur.ai/free/en/papers/ahuja-2024-megaverse-benchmarking-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/)**Sanchit Ahuja, Divyanshu Aggarwal, Varun Gumma, et al.*2024 · 74 citations*
111. [**Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integration arXiv:2306.09093**Chenyang Lyu, Minghao Wu, Longyue Wang, et al.*2023 · 74 citations*](https://md.research.elamur.ai/free/en/papers/lyu-2023-macaw-llm-multi-modal-language/)
112. **[Cultural Alignment in Large Language Models: An Explanatory Analysis Based on Hofstede's Cultural Dimensions](https://md.research.elamur.ai/free/en/papers/masoud-2025-cultural-alignment-large-language-models/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Reem I. Masoud, Ziquan Liu, Martin Ferianc, et al.*2025 · 73 citations*
113. [**AlignBench: Benchmarking Chinese Alignment of Large Language Models arXiv:2311.18743**Xiao Liu, Xuanyu Lei, Shengyuan Wang, et al.*2023 · 73 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-alignbench-benchmarking-chinese-alignment/)
114. [**Gecko: Versatile Text Embeddings Distilled from Large Language Models arXiv:2403.20327**Jinhyuk Lee, Zhuyun Dai, Xiaoqi Ren, et al.*2024 · 72 citations*](https://md.research.elamur.ai/free/en/papers/lee-2024-gecko-versatile-text-embeddings-distilled/)
115. [**Chain-of-Thought Hub: A Continuous Effort to Measure Large Language Models' Reasoning Performance arXiv:2305.17306**Yao Fu, Litu Ou, Mingyu Chen, et al.*2023 · 69 citations*](https://md.research.elamur.ai/free/en/papers/fu-2023-chain-thought-hub-continuous-effort/)
116. [**Contrastive Decoding Improves Reasoning in Large Language Models arXiv:2309.09117**Sean O'Brien, Mike Lewis*2023 · 67 citations*](https://md.research.elamur.ai/free/en/papers/o-brien-2023-contrastive-decoding-improves-reasoning-large/)
117. [**Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning arXiv:2309.05444**Ted Zadouri, Ahmet Ustun, Arash Ahmadian, et al.*2023 · 66 citations*](https://md.research.elamur.ai/free/en/papers/zadouri-2023-pushing-mixture-experts-limit-extremely/)
118. **[One-Shot Learning as Instruction Data Prospector for Large Language Models](https://md.research.elamur.ai/free/en/papers/li-2024-one-shot-learning-as-instruction/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yunshui Li, Binyuan Hui, Xiaobo Xia, et al.*2024 · 61 citations*
119. [**OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models arXiv:2402.01739**Fuzhao Xue, Zian Zheng, Yao Fu, et al.*2024 · 61 citations*](https://md.research.elamur.ai/free/en/papers/xue-2024-openmoe-early-effort-open-mixture/)
120. **[Bridging the Preference Gap between Retrievers and LLMs](https://md.research.elamur.ai/free/en/papers/ke-2024-bridging-preference-gap-between-retrievers/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zixuan Ke, Weize Kong, Cheng Li, et al.*2024 · 45 citations*
121. [**MBR and QE Finetuning: Training-time Distillation of the Best and Most Expensive Decoding Methods arXiv:2309.10966**Mara Finkelstein; Subhajit Naskar; Mehdi Mirzazadeh; Apurva Shah;, Markus Freitag*2023 · 30 citations*](https://md.research.elamur.ai/free/en/papers/finkelstein-2023-mbr-qe-finetuning-training-time/)
122. **[Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations](https://md.research.elamur.ai/free/en/papers/hagele-2024-scaling-laws-compute-optimal-training/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Alexander Hägele, Elie Bakouch, Atli Kosson, et al.*2024 · 26 citations*
123. **[M-MAD: Multidimensional Multi-Agent Debate for Advanced Machine Translation Evaluation](https://md.research.elamur.ai/free/en/papers/feng-2025-m-mad-multidimensional-multi-agent/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhaopeng Feng, Jiayuan Su, Jiamei Zheng, et al.*2025 · 24 citations*
124. [**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/)
125. **[A Rationale-centric Counterfactual Data Augmentation Method for Cross-Document Event Coreference Resolution](https://md.research.elamur.ai/free/en/papers/ding-2024-rationale-centric-counterfactual-data-augmentation/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Bowen Ding, Qingkai Min, Shengkun Ma, et al.*2024 · 17 citations*
126. [**Skywork-Math: Data Scaling Laws for Mathematical Reasoning in Large Language Models — The Story Goes On arXiv:2407.08348**Liang Zeng, Liangjun Zhong, Liang Zhao, et al.*2024 · 14 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2024-skywork-math-data-scaling-laws/)
127. [**Chinchilla Scaling: A replication attempt arXiv:2404.10102**Tamay Besiroglu, Ege Erdil, Matthew Barnett, et al.*2024 · 12 citations*](https://md.research.elamur.ai/free/en/papers/besiroglu-2024-chinchilla-scaling-replication-attempt/)
128. **[Beyond Random Sampling: Efficient Language Model Pretraining via Curriculum Learning](https://md.research.elamur.ai/free/en/papers/zhang-2026-beyond-random-sampling-efficient-language/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Yang Zhang, Amr Mohamed, Hadi Abdine, et al.*2026 · 7 citations*
129. **[Survey on Key Technologies for Large Language Model Pre-training Systems (大语言模型预训练系统关键技术综述)](https://md.research.elamur.ai/free/en/papers/gao-2025-key-technologies-large/) [Journal of Software](https://md.research.elamur.ai/free/en/venues/journal-of-software/)**Yanjie Gao (高彦杰), Yueguo Chen (陈跃国)*2025*
130. [**From Foundation Intelligence to General Intelligence: Status and Prospects of Large-Model-Based GenAI and AGI (从基础智能到通用智能: 基于大模型的GenAI和AGI之现状与展望) Acta Automatica Sinica (自动化学报)**Qinghai Miao (缪青海), Xingxia Wang (王兴霞), Jing Yang (杨静), et al.*2024*](https://md.research.elamur.ai/free/en/papers/miao-2024-foundation-intelligence-general/)
131. **[Robustness of GPT Large Language Models on Natural Language Processing Tasks (GPT系列大语言模型在自然语言处理任务中的鲁棒性)](https://md.research.elamur.ai/free/en/papers/chen-2024-robustness-gpt-large/) [Journal of Computer Research and Development](https://md.research.elamur.ai/free/en/venues/journal-of-computer-research-and-development/)**Xuanting Chen (陈炫婷), Junjie Ye (叶俊杰), Can Zu (祖璨), et al.*2024*

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