---
title: "Who cites Susan Zhang — page 2"
description: "1,735 papers of the Elamur Research library cite the works of Susan Zhang, found in their bibliographies · page 2 of 2 · 101–166; the most cited first."
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---
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# Who cites Susan Zhang

1,741 citations of their works from 1,735 papers of our library (of the 140,884 whose bibliographies we read) · 166 with a page here, the most cited first · page 2 of 2 · 101–166 · [← Susan Zhang](https://md.research.elamur.ai/free/en/authors/susan-zhang/)

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

101. [**Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models**Kushal Tirumala, Aram H. Markosyan, Luke Zettlemoyer, et al.*2022 · 183 citations*](https://md.research.elamur.ai/free/en/papers/tirumala-2022-memorization-overfitting-analyzing-training-dynamics/)
102. [**GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models**Archiki Prasad, Peter Hase, Xiang Zhou, et al.*2023 · 180 citations*](https://md.research.elamur.ai/free/en/papers/prasad-2023-grips-gradient-free-edit-based/)
103. [**On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning**Omar Shaikh, Hongxin Zhang, William Held, et al.*2023 · 180 citations*](https://md.research.elamur.ai/free/en/papers/shaikh-2023-second-thought-let-s-not/)
104. [**Adapting Language Models to Compress Contexts**Alexis Chevalier, Alexander Wettig, Anirudh Ajith, et al.*2023 · 179 citations*](https://md.research.elamur.ai/free/en/papers/chevalier-2023-adapting-language-models-compress-contexts/)
105. [**Eliciting Latent Predictions from Transformers with the Tuned Lens**Nora Belrose, Igor Ostrovsky, Lev McKinney, et al.*2023 · 179 citations*](https://md.research.elamur.ai/free/en/papers/belrose-2023-eliciting-latent-predictions-transformers-tuned/)
106. [**Evaluating Large Language Models: A Comprehensive Survey**Zishan Guo, Renren Jin, Chuang Liu, et al.*2023 · 177 citations*](https://md.research.elamur.ai/free/en/papers/guo-2023-evaluating-large-language-models-comprehensive/)
107. [**DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text**Jinyan Su, Terry Zhuo, Di Wang, et al.*2023 · 172 citations*](https://md.research.elamur.ai/free/en/papers/su-2023-detectllm-leveraging-log-rank-information/)
108. [**Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity**Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, et al.*2023 · 169 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-survey-factuality-large-language-models/)
109. [**SqueezeLLM: Dense-and-Sparse Quantization**Sehoon Kim, Coleman Hooper, Amir Gholami, et al.*2023 · 168 citations*](https://md.research.elamur.ai/free/en/papers/kim-2023-squeezellm-dense-sparse-quantization/)
110. [**Speak, Memory: An Archaeology of Books Known to ChatGPT/GPT-4**Kent K. Chang, Mackenzie Cramer, Sandeep Soni, et al.*2023 · 166 citations*](https://md.research.elamur.ai/free/en/papers/chang-2023-speak-memory-archaeology-books-known/)
111. [**Large Language Model Unlearning**Yuanshun Yao, Xiaojun Xu, Yang Liu*2023 · 165 citations*](https://md.research.elamur.ai/free/en/papers/yao-2023-large-language-model-unlearning/)
112. [**Do All Languages Cost the Same? Tokenization in the Era of Commercial Language Models**Orevaoghene Ahia, Sachin Kumar, Hila Gonen, et al.*2023 · 164 citations*](https://md.research.elamur.ai/free/en/papers/ahia-2023-do-all-languages-cost-same/)
113. [**Quark: Controllable Text Generation with Reinforced Unlearning**Ximing Lu, Sean Welleck, Jack Hessel, et al.*2022 · 164 citations*](https://md.research.elamur.ai/free/en/papers/lu-2022-quark-controllable-text-generation-reinforced/)
114. [**Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents**Zihao Wang, Shaofei Cai, Guanzhou Chen, et al.*2023 · 161 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-describe-explain-plan-select-interactive/)
115. [**TheoremQA: A Theorem-driven Question Answering Dataset**Wenhu Chen, Ming Yin, Max Ku, et al.*2023 · 154 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-theoremqa-theorem-driven-question-answering/)
116. [**SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training**Tianzhe Chu, Yuexiang Zhai, Jihan Yang, et al.*2025 · 151 citations*](https://md.research.elamur.ai/free/en/papers/chu-2025-sft-memorizes-rl-generalizes-comparative/)
117. [**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/)
118. [**Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigation**Niels Mundler, Jingxuan He, Slobodan Jenko, et al.*2023 · 150 citations*](https://md.research.elamur.ai/free/en/papers/mundler-2023-self-contradictory-hallucinations-large-language/)
119. [**ProsocialDialog: A Prosocial Backbone for Conversational Agents**Hyunwoo Kim, Youngjae Yu, Liwei Jiang, et al.*2022 · 149 citations*](https://md.research.elamur.ai/free/en/papers/kim-2022-prosocialdialog-prosocial-backbone-conversational-agents/)
120. [**Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature**Guangsheng Bao, Yanbin Zhao, Zhiyang Teng, et al.*2023 · 146 citations*](https://md.research.elamur.ai/free/en/papers/bao-2023-fast-detectgpt-efficient-zero-shot/)
121. [**Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding**Heming Xia, Zhe Yang, Qingxiu Dong, et al.*2024 · 145 citations*](https://md.research.elamur.ai/free/en/papers/xia-2024-unlocking-efficiency-large-language-model/)
122. [**The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset**Hugo Laurençon, Lucile Saulnier, Thomas Wang, et al.*2023 · 145 citations*](https://md.research.elamur.ai/free/en/papers/lauren-ccon-2023-bigscience-roots-corpus-1-6tb/)
123. [**In-Context Learning for Few-Shot Dialogue State Tracking**Yushi Hu, Chia-Hsuan Lee, Tianbao Xie, et al.*2022 · 144 citations*](https://md.research.elamur.ai/free/en/papers/hu-2022-context-learning-few-shot-dialogue/)
124. [**Is GPT-3 a Good Data Annotator?**Bosheng Ding, Chengwei Qin, Linlin Liu, et al.*2022 · 144 citations*](https://md.research.elamur.ai/free/en/papers/ding-2022-gpt-3-good-data-annotator/)
125. [**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/)
126. [**JudgeLM: Fine-tuned Large Language Models are Scalable Judges**Lianghui Zhu, Xinggang Wang, Xinlong Wang*2023 · 141 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2023-judgelm-fine-tuned-large-language/)
127. [**A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generation**Neeraj Varshney; Wenlin Yao; Hongming Zhang; Jianshu Chen;, Dong Yu*2023 · 139 citations*](https://md.research.elamur.ai/free/en/papers/varshney-2023-stitch-time-saves-nine-detecting/)
128. [**Symbolic Chain-of-Thought Distillation: Small Models Can Also “Think” Step-by-Step**Liunian Harold Li, Jack Hessel, Youngjae Yu, et al.*2023 · 139 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-symbolic-chain-thought-distillation-small/)
129. [**Investigating Data Contamination in Modern Benchmarks for Large Language Models**Chunyuan Deng, Yilun Zhao, Xiangru Tang, et al.*2024 · 137 citations*](https://md.research.elamur.ai/free/en/papers/deng-2024-investigating-data-contamination-modern-benchmarks/)
130. [**Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models**Bernd Bohnet, Vinh Q. Tran, Pat Verga, et al.*2022 · 136 citations*](https://md.research.elamur.ai/free/en/papers/bohnet-2022-attributed-question-answering-evaluation-modeling/)
131. [**InFoBench: Evaluating Instruction Following Ability in Large Language Models**Yiwei Qin, Kaiqiang Song, Yebowen Hu, et al.*2024 · 133 citations*](https://md.research.elamur.ai/free/en/papers/qin-2024-infobench-evaluating-instruction-following-ability/)
132. [**Understanding the Effects of RLHF on LLM Generalisation and Diversity**Robert Kirk, Ishita Mediratta, Christoforos Nalmpantis, et al.*2023 · 131 citations*](https://md.research.elamur.ai/free/en/papers/kirk-2023-understanding-effects-rlhf-llm-generalisation/)
133. [**MathDial: A Dialogue Tutoring Dataset with Rich Pedagogical Properties Grounded in Math Reasoning Problems**Jakub Macina, Nico Daheim, Sankalan Chowdhury, et al.*2023 · 130 citations*](https://md.research.elamur.ai/free/en/papers/macina-2023-mathdial-dialogue-tutoring-dataset-rich/)
134. [**Analyzing Transformers in Embedding Space**Guy Dar, Mor Geva, Ankit Gupta, et al.*2023 · 129 citations*](https://md.research.elamur.ai/free/en/papers/dar-2023-analyzing-transformers-embedding-space/)
135. [**Deductive Verification of Chain-of-Thought Reasoning**Zhan Ling, Yunhao Fang, Xuanlin Li, et al.*2023 · 128 citations*](https://md.research.elamur.ai/free/en/papers/ling-2023-deductive-verification-chain-thought-reasoning/)
136. [**Knowledge Unlearning for Mitigating Privacy Risks in Language Models**Joel Jang, Dongkeun Yoon, Sohee Yang, et al.*2022 · 126 citations*](https://md.research.elamur.ai/free/en/papers/jang-2022-knowledge-unlearning-mitigating-privacy-risks/)
137. [**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/)
138. [**The case for 4-bit precision: k-bit Inference Scaling Laws**Tim Dettmers, Luke Zettlemoyer*2022 · 119 citations*](https://md.research.elamur.ai/free/en/papers/dettmers-2022-case-4-bit-precision-k/)
139. [**Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering**Jinheon Baek, Alham Fikri Aji, Amir Saffari*2023 · 118 citations*](https://md.research.elamur.ai/free/en/papers/baek-2023-knowledge-augmented-language-model-prompting/)
140. [**Chameleon: Mixed-Modal Early-Fusion Foundation Models**Chameleon Team*2024 · 117 citations*](https://md.research.elamur.ai/free/en/papers/team-2024-chameleon-mixed-modal-early-fusion/)
141. [**GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction**Oscar Sainz, Iker Garcia-Ferrero, Rodrigo Agerri, et al.*2023 · 114 citations*](https://md.research.elamur.ai/free/en/papers/sainz-2023-gollie-annotation-guidelines-improve-zero/)
142. [**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/)
143. [**Inverse Scaling: When Bigger Isn't Better**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/)
144. [**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/)
145. [**Dictionary-based Phrase-level Prompting of Large Language Models for Machine Translation**Marjan Ghazvininejad, Hila Gonen, Luke Zettlemoyer*2023 · 95 citations*](https://md.research.elamur.ai/free/en/papers/ghazvininejad-2023-dictionary-based-phrase-level-prompting/)
146. [**SemDeDup: Data-efficient learning at web-scale through semantic deduplication**Amro Abbas, Kushal Tirumala, Dániel Simig, et al.*2023 · 94 citations*](https://md.research.elamur.ai/free/en/papers/abbas-2023-semdedup-data-efficient-learning-web/)
147. [**InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model**Xiaoyi Dong, Pan Zhang, Yuhang Zang, et al.*2024 · 92 citations*](https://md.research.elamur.ai/free/en/papers/dong-2024-internlm-xcomposer2-mastering-free/)
148. [**The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs**Laura Ruis, Akbir Khan, Stella Biderman, et al.*2022 · 86 citations*](https://md.research.elamur.ai/free/en/papers/ruis-2022-goldilocks-pragmatic-understanding-fine-tuning/)
149. [**Chain-of-Dictionary Prompting Elicits Translation in Large Language Models**Hongyuan Lu, Haoran Yang, Haoyang Huang, et al.*2024 · 81 citations*](https://md.research.elamur.ai/free/en/papers/lu-2024-chain-dictionary-prompting-elicits-translation/)
150. [**D4: Improving LLM Pretraining via Document De-Duplication and Diversification**Kushal Tirumala, Daniel Simig, Armen Aghajanyan, et al.*2023 · 79 citations*](https://md.research.elamur.ai/free/en/papers/tirumala-2023-d4-improving-llm-pretraining-document/)
151. [**Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies**Chaofan Tao, Qian Liu, Longxu Dou, et al.*2024 · 42 citations*](https://md.research.elamur.ai/free/en/papers/tao-2024-scaling-laws-vocabulary-larger-models/)
152. [**Scaling Laws for Generative Mixed-Modal Language Models**Armen Aghajanyan, Lili Yu, Alexis Conneau, et al.*2023 · 39 citations*](https://md.research.elamur.ai/free/en/papers/aghajanyan-2023-scaling-laws-generative-mixed-modal/)
153. [**Towards Cross-Tokenizer Distillation: the Universal Logit Distillation Loss for LLMs**Nicolas Boizard, Kevin El Haddad, Celine Hudelot, et al.*2024 · 34 citations*](https://md.research.elamur.ai/free/en/papers/boizard-2024-towards-cross-tokenizer-distillation-universal/)
154. [**Observational Scaling Laws and the Predictability of Language Model Performance**Yangjun Ruan, Chris J. Maddison, Tatsunori Hashimoto*2024 · 28 citations*](https://md.research.elamur.ai/free/en/papers/ruan-2024-observational-scaling-laws-predictability-language/)
155. [**Exploring the Relationship between LLM Hallucinations and Prompt Linguistic Nuances: Readability, Formality, and Concreteness**Vipula Rawte, Prachi Priya, S.M Towhidul Islam Tonmoy, et al.*2023 · 27 citations*](https://md.research.elamur.ai/free/en/papers/rawte-2023-exploring-relationship-between-llm-hallucinations/)
156. [**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/)
157. [**Low-Resource Authorship Style Transfer: Can Non-Famous Authors Be Imitated?**Ajay Patel, Nicholas Andrews, Chris Callison-Burch*2022 · 24 citations*](https://md.research.elamur.ai/free/en/papers/patel-2022-low-resource-authorship-style-transfer/)
158. [**Few-Shot Anaphora Resolution in Scientific Protocols via Mixtures of In-Context Experts**Nghia T. Le, Fan Bai, Alan Ritter*2022 · 20 citations*](https://md.research.elamur.ai/free/en/papers/le-2022-few-shot-anaphora-resolution-scientific/)
159. [**Resolving Discrepancies in Compute-Optimal Scaling of Language Models**Tomer Porian, Mitchell Wortsman, Jenia Jitsev, et al.*2024 · 14 citations*](https://md.research.elamur.ai/free/en/papers/porian-2024-resolving-discrepancies-compute-optimal-scaling/)
160. [**Constituency Parsing using LLMs**Xuefeng Bai, Jialong Wu, Yulong Chen, et al.*2023 · 14 citations*](https://md.research.elamur.ai/free/en/papers/bai-2023-constituency-parsing-using-llms/)
161. [**Bridging the Resource Gap: Exploring the Efficacy of English and Multilingual LLMs for Swedish**Oskar Holmström, Jenny Kunz, Marco Kuhlmann*2023 · 8 citations*](https://md.research.elamur.ai/free/en/papers/holmstrom-2023-bridging-resource-gap-exploring-efficacy/)
162. [**Language Models Learn Constructional Semantics, Not To Mention Syntax: Investigating LM Understanding of Paired-Focus Constructions**Wesley Scivetti, Ethan Gotlieb Wilcox, Nathan Schneider, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/scivetti-2026-language-models-learn-constructional-semantics/)
163. [**Spotlights and Blindspots: Evaluating Machine-Generated Text Detection**Kevin Stowe, Kailash Patil*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/stowe-2026-spotlights-blindspots-evaluating-machine-generated/)
164. [**HatePrototypes: Interpretable and Transferable Representations for Implicit and Explicit Hate Speech Detection**Irina Proskurina, Marc-Antoine Carpentier, Julien Velcin*2026*](https://md.research.elamur.ai/free/en/papers/proskurina-2026-hateprototypes-interpretable-transferable-representations-implicit/)
165. [**Modeling the Memory-Surprisal Trade-Off over Time: Communicative Efficiency Decreases with Lexico-Grammatical Change in Scientific English**Julius Steuer, Marie-Pauline Krielke, Stefania Degaetano-Ortlieb, et al.*2026*](https://md.research.elamur.ai/free/en/papers/steuer-2026-modeling-memory-surprisal-trade-off/)
166. [**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/)

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