---
title: "Who cites David R. So — page 2"
description: "1,336 papers of the Elamur Research library cite the works of David R. So, found in their bibliographies · page 2 of 2 · 101–141; the most cited first."
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---
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# Who cites David R. So

1,371 citations of their works from 1,336 papers of our library (of the 140,884 whose bibliographies we read) · 141 with a page here, the most cited first · page 2 of 2 · 101–141 · [← David R. So](https://md.research.elamur.ai/free/en/authors/david-r-so/)

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

101. [**Catastrophic Jailbreak of Open-source LLMs via Exploiting Generation**Yangsibo Huang, Samyak Gupta, Mengzhou Xia, et al.*2023 · 162 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-catastrophic-jailbreak-open-source-llms/)
102. [**GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts**Jiahao Yu, Xingwei Lin, Zheng Yu, et al.*2023 · 162 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-gptfuzzer-red-teaming-large-language/)
103. [**Orca 2: Teaching Small Language Models How to Reason**Arindam Mitra, Luciano Del Corro, Shweti Mahajan, et al.*2023 · 160 citations*](https://md.research.elamur.ai/free/en/papers/mitra-2023-orca-2-teaching-small-language/)
104. [**The Devil Is in the Errors: Leveraging Large Language Models for Fine-grained Machine Translation Evaluation**Patrick Fernandes, Daniel Deutsch, Mara Finkelstein, et al.*2023 · 156 citations*](https://md.research.elamur.ai/free/en/papers/fernandes-2023-devil-is-errors-leveraging-large/)
105. [**HunyuanVideo: A Systematic Framework For Large Video Generative Models**Weijie Kong, Qi Tian, Zijian Zhang, et al.*2024 · 155 citations*](https://md.research.elamur.ai/free/en/papers/kong-2024-hunyuanvideo-systematic-framework-large/)
106. [**Results of WMT23 Metrics Shared Task: Metrics Might Be Guilty but References Are Not Innocent**Markus Freitag, Nitika Mathur, Chi-kiu Lo, et al.*2023 · 155 citations*](https://md.research.elamur.ai/free/en/papers/freitag-2023-results-wmt23-metrics-shared-task/)
107. [**Language-Specific Neurons: The Key to Multilingual Capabilities in Large Language Models**Tianyi Tang, Wenyang Luo, Haoyang Huang, et al.*2024 · 151 citations*](https://md.research.elamur.ai/free/en/papers/tang-2024-language-specific-neurons-key-multilingual/)
108. [**BLEnD: A Benchmark for LLMs on Everyday Knowledge in Diverse Cultures and Languages**Junho Myung, Nayeon Lee, Yi Zhou, et al.*2024 · 147 citations*](https://md.research.elamur.ai/free/en/papers/myung-2024-blend-benchmark-llms-everyday-knowledge/)
109. [**How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition**Guanting Dong, Hongyi Yuan, Keming Lu, et al.*2024 · 145 citations*](https://md.research.elamur.ai/free/en/papers/dong-2024-how-abilities-large-language-models/)
110. [**RA-DIT: Retrieval-Augmented Dual Instruction Tuning**Xi Victoria Lin, Xilun Chen, Mingda Chen, et al.*2023 · 145 citations*](https://md.research.elamur.ai/free/en/papers/lin-2023-ra-dit-retrieval-augmented-dual/)
111. [**Iterative Reasoning Preference Optimization**Richard Yuanzhe Pang, Weizhe Yuan, Kyunghyun Cho, et al.*2024 · 144 citations*](https://md.research.elamur.ai/free/en/papers/pang-2024-iterative-reasoning-preference-optimization/)
112. [**MiniLLM: On-Policy Distillation of Large Language Models**Yuxian Gu, Li Dong, Furu Wei, et al.*2023 · 143 citations*](https://md.research.elamur.ai/free/en/papers/gu-2023-minillm-policy-distillation-large-language/)
113. [**PMC-LLaMA: Towards Building Open-source Language Models for Medicine**Chaoyi Wu, Weixiong Lin, Xiaoman Zhang, et al.*2023 · 142 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-pmc-llama-towards-building-open/)
114. [**Error Analysis Prompting Enables Human-Like Translation Evaluation in Large Language Models**Qingyu Lu, Baopu Qiu, Liang Ding, et al.*2024 · 135 citations*](https://md.research.elamur.ai/free/en/papers/lu-2024-error-analysis-prompting-enables-human/)
115. [**Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models**Avi Singh, John D. Co-Reyes, Rishabh Agarwal, et al.*2023 · 134 citations*](https://md.research.elamur.ai/free/en/papers/singh-2023-human-data-scaling-self-training/)
116. [**Large Language Model Alignment: A Survey**Tianhao Shen, Renren Jin, Yufei Huang, et al.*2023 · 123 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-large-language-model-alignment-survey/)
117. [**AlphaMath Almost Zero: Process Supervision without Process**Guoxin Chen, Minpeng Liao, Chengxi Li, et al.*2024 · 119 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-alphamath-almost-zero-process-supervision/)
118. [**MetricX-23: The Google Submission to the WMT 2023 Metrics Shared Task**Juraj Juraska, Mara Finkelstein, Daniel Deutsch, et al.*2023 · 118 citations*](https://md.research.elamur.ai/free/en/papers/juraska-2023-metricx-23-google-submission-wmt/)
119. [**Pretraining Language Models with Human Preferences**Tomasz Korbak, Kejian Shi, Angelica Chen, et al.*2023 · 117 citations*](https://md.research.elamur.ai/free/en/papers/korbak-2023-pretraining-language-models-human-preferences/)
120. [**From Persona to Personalization: A Survey on Role-Playing Language Agents**Jiangjie Chen, Xintao Wang, Rui Xu, et al.*2024 · 114 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-persona-personalization-survey-role-playing/)
121. [**ALLaM: Large Language Models for Arabic and English**M Saiful Bari, Yazeed Alnumay, Norah A. Alzahrani, et al.*2024 · 113 citations*](https://md.research.elamur.ai/free/en/papers/bari-2024-allam-large-language-models-arabic/)
122. [**DistiLLM: Towards Streamlined Distillation for Large Language Models**Jongwoo Ko, Sungnyun Kim, Tianyi Chen, et al.*2024 · 111 citations*](https://md.research.elamur.ai/free/en/papers/ko-2024-distillm-towards-streamlined-distillation-large/)
123. [**Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verification**Aojun Zhou, Ke Wang, Zimu Lu, et al.*2023 · 109 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2023-solving-challenging-math-word-problems/)
124. [**Rethinking Benchmark and Contamination for Language Models with Rephrased Samples**Shuo Yang, Wei-Lin Chiang, Lianmin Zheng, et al.*2023 · 100 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-rethinking-benchmark-contamination-language-models/)
125. [**MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning**Ke Wang, Houxing Ren, Aojun Zhou, et al.*2023 · 98 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-mathcoder-seamless-code-integration-llms/)
126. [**Skywork: A More Open Bilingual Foundation Model**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/)
127. [**"I'd rather just go to bed": Understanding Indirect Answers**Annie Louis, Dan Roth, Filip Radlinski*2020 · 70 citations*](https://md.research.elamur.ai/free/en/papers/louis-2020-i-d-rather-just-go/)
128. [**Bridging the Preference Gap between Retrievers and LLMs**Zixuan Ke, Weize Kong, Cheng Li, et al.*2024 · 45 citations*](https://md.research.elamur.ai/free/en/papers/ke-2024-bridging-preference-gap-between-retrievers/)
129. [**MBR and QE Finetuning: Training-time Distillation of the Best and Most Expensive Decoding Methods**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/)
130. [**M-MAD: Multidimensional Multi-Agent Debate for Advanced Machine Translation Evaluation**Zhaopeng Feng, Jiayuan Su, Jiamei Zheng, et al.*2025 · 24 citations*](https://md.research.elamur.ai/free/en/papers/feng-2025-m-mad-multidimensional-multi-agent/)
131. [**Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations**Alexander Hägele, Elie Bakouch, Atli Kosson, et al.*2024 · 24 citations*](https://md.research.elamur.ai/free/en/papers/hagele-2024-scaling-laws-compute-optimal-training/)
132. [**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/)
133. [**A Rationale-centric Counterfactual Data Augmentation Method for Cross-Document Event Coreference Resolution**Bowen Ding, Qingkai Min, Shengkun Ma, et al.*2024 · 17 citations*](https://md.research.elamur.ai/free/en/papers/ding-2024-rationale-centric-counterfactual-data-augmentation/)
134. [**Skywork-Math: Data Scaling Laws for Mathematical Reasoning in Large Language Models — The Story Goes On**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/)
135. [**Chinchilla Scaling: A replication attempt**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/)
136. [**Beyond Random Sampling: Efficient Language Model Pretraining via Curriculum Learning**Yang Zhang, Amr Mohamed, Hadi Abdine, et al.*2026 · 7 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2026-beyond-random-sampling-efficient-language/)
137. [**Automatic Evaluate Dialogue Appropriateness by Using Dialogue Act**Bao Chen, Yuanjie Wang, Zeming Liu, et al.*2023 · 6 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-automatic-evaluate-dialogue-appropriateness-dialogue/)
138. [**AlcLaM: Arabic Dialectal Language Model**Murtadha Ahmed, Saghir Alfasly, Bo Wen, et al.*2024 · 1 citation*](https://md.research.elamur.ai/free/en/papers/ahmed-2024-alclam-arabic-dialectal-language-model/)
139. [**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)**Yanjie Gao (高彦杰), Yueguo Chen (陈跃国)*2025*](https://md.research.elamur.ai/free/en/papers/gao-2025-key-technologies-large/)
140. [**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)**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/)
141. [**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)**Xuanting Chen (陈炫婷), Junjie Ye (叶俊杰), Can Zu (祖璨), et al.*2024*](https://md.research.elamur.ai/free/en/papers/chen-2024-robustness-gpt-large/)

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