---
title: "Who cites Vincent Zhao — page 2"
description: "1,207 papers of the Elamur Research library cite the works of Vincent Zhao, found in their bibliographies · page 2 of 2 · 101–151; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/vincent-zhao/cited-by/page/2/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/vincent-zhao/cited-by/page/2/)

[home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Vincent Zhao](https://md.research.elamur.ai/free/en/authors/vincent-zhao/) / [cited by](https://md.research.elamur.ai/free/en/authors/vincent-zhao/cited-by/) / page 2

# Who cites Vincent Zhao

1,303 citations of their works from 1,207 papers of our library (of the 140,884 whose bibliographies we read) · 151 with a page here, the most cited first · page 2 of 2 · 101–151 · [← Vincent Zhao](https://md.research.elamur.ai/free/en/authors/vincent-zhao/)

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

101. [**NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models**Chankyu Lee, Rajarshi Roy, Mengyao Xu, et al.*2024 · 219 citations*](https://md.research.elamur.ai/free/en/papers/lee-2024-nv-embed-improved-techniques-training/)
102. [**Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP**Omar Khattab, Keshav Santhanam, Xiang Lisa Li, et al.*2022 · 211 citations*](https://md.research.elamur.ai/free/en/papers/khattab-2022-demonstrate-search-predict-composing-retrieval/)
103. [**Teaching language models to support answers with verified quotes**Jacob Menick, Maja Trebacz, Vladimir Mikulik, et al.*2022 · 211 citations*](https://md.research.elamur.ai/free/en/papers/menick-2022-teaching-language-models-support-answers/)
104. [**PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change**Karthik Valmeekam, Matthew Marquez, Alberto Olmo, et al.*2022 · 209 citations*](https://md.research.elamur.ai/free/en/papers/valmeekam-2022-planbench-extensible-benchmark-evaluating-large/)
105. [**RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models**Cheng Niu, Yuanhao Wu, Juno Zhu, et al.*2024 · 204 citations*](https://md.research.elamur.ai/free/en/papers/niu-2024-ragtruth-hallucination-corpus-developing-trustworthy/)
106. [**Internet-augmented language models through few-shot prompting for open-domain question answering**Angeliki Lazaridou, Elena Gribovskaya, Wojciech Stokowiec, et al.*2022 · 204 citations*](https://md.research.elamur.ai/free/en/papers/lazaridou-2022-internet-augmented-language-models-through/)
107. [**A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets**Md Tahmid Rahman Laskar, M Saiful Bari, Mizanur Rahman, et al.*2023 · 198 citations*](https://md.research.elamur.ai/free/en/papers/laskar-2023-systematic-study-comprehensive-evaluation-chatgpt/)
108. [**One Embedder, Any Task: Instruction-Finetuned Text Embeddings**Hongjin Su, Weijia Shi, Jungo Kasai, et al.*2022 · 191 citations*](https://md.research.elamur.ai/free/en/papers/su-2022-one-embedder-any-task-instruction/)
109. [**SGPT: GPT Sentence Embeddings for Semantic Search**Niklas Muennighoff*2022 · 187 citations*](https://md.research.elamur.ai/free/en/papers/muennighoff-2022-sgpt-gpt-sentence-embeddings-semantic/)
110. [**Knowledge Conflicts for LLMs: A Survey**Rongwu Xu, Zehan Qi, Zhijiang Guo, et al.*2024 · 179 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-knowledge-conflicts-llms-survey/)
111. [**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/)
112. [**LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers**Theo X. Olausson, Alex Gu, Benjamin Lipkin, et al.*2023 · 171 citations*](https://md.research.elamur.ai/free/en/papers/olausson-2023-linc-neurosymbolic-approach-logical-reasoning/)
113. [**Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs**Simone Balloccu, Patrícia Schmidtová, Mateusz Lango, et al.*2024 · 170 citations*](https://md.research.elamur.ai/free/en/papers/balloccu-2024-leak-cheat-repeat-data-contamination/)
114. [**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/)
115. [**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/)
116. [**Promptagator: Few-shot Dense Retrieval From 8 Examples**Zhuyun Dai, Vincent Y. Zhao, Ji Ma, et al.*2022 · 167 citations*](https://md.research.elamur.ai/free/en/papers/dai-2022-promptagator-few-shot-dense-retrieval/)
117. [**Generative Representational Instruction Tuning**Niklas Muennighoff, Hongjin Su, Liang Wang, et al.*2024 · 165 citations*](https://md.research.elamur.ai/free/en/papers/muennighoff-2024-generative-representational-instruction-tuning/)
118. [**Long-form factuality in large language models**Jerry Wei, Chengrun Yang, Xinying Song, et al.*2024 · 162 citations*](https://md.research.elamur.ai/free/en/papers/wei-2024-long-form-factuality-large-language/)
119. [**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/)
120. [**Reasoning with Language Model Prompting: A Survey**Shuofei Qiao, Yixin Ou, Ningyu Zhang, et al.*2022 · 160 citations*](https://md.research.elamur.ai/free/en/papers/qiao-2022-reasoning-language-model-prompting-survey/)
121. [**MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents**Liyan Tang, Philippe Laban, Greg Durrett*2024 · 159 citations*](https://md.research.elamur.ai/free/en/papers/tang-2024-minicheck-efficient-fact-checking-llms/)
122. [**When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMs**Ryo Kamoi, Yusen Zhang, Nan Zhang, et al.*2024 · 159 citations*](https://md.research.elamur.ai/free/en/papers/kamoi-2024-when-can-llms-actually-correct/)
123. [**FacTool: Factuality Detection in Generative AI — A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios**I-Chun Chern, Steffi Chern, Shiqi Chen, et al.*2023 · 158 citations*](https://md.research.elamur.ai/free/en/papers/chern-2023-factool-factuality-detection-generative-ai/)
124. [**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/)
125. [**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/)
126. [**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/)
127. [**ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings**Shibo Hao, Tianyang Liu, Zhen Wang, et al.*2023 · 142 citations*](https://md.research.elamur.ai/free/en/papers/hao-2023-toolkengpt-augmenting-frozen-language-models/)
128. [**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/)
129. [**Efficient Training of Language Models to Fill in the Middle**Mohammad Bavarian, Heewoo Jun, Nikolas Tezak, et al.*2022 · 137 citations*](https://md.research.elamur.ai/free/en/papers/bavarian-2022-efficient-training-language-models-fill/)
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. [**Task-aware Retrieval with Instructions**Akari Asai, Timo Schick, Patrick Lewis, et al.*2023 · 131 citations*](https://md.research.elamur.ai/free/en/papers/asai-2023-task-aware-retrieval-instructions/)
132. [**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/)
133. [**Secrets of RLHF in Large Language Models Part I: PPO**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/)
134. [**Fanar: An Arabic-Centric Multimodal Generative AI Platform**Fanar Team: Ummar Abbas, Mohammad Shahmeer Ahmad, Firoj Alam, et al.*2025 · 127 citations*](https://md.research.elamur.ai/free/en/papers/abbas-2025-fanar-arabic-centric-multimodal-generative/)
135. [**A Recipe For Arbitrary Text Style Transfer with Large Language Models**Emily Reif, Daphne Ippolito, Ann Yuan, et al.*2021 · 125 citations*](https://md.research.elamur.ai/free/en/papers/reif-2021-recipe-arbitrary-text-style-transfer/)
136. [**Nomic Embed: Training a Reproducible Long Context Text Embedder**Zach Nussbaum, John X. Morris, Brandon Duderstadt, et al.*2024 · 123 citations*](https://md.research.elamur.ai/free/en/papers/nussbaum-2024-nomic-embed-training-reproducible-long/)
137. [**Fine-tuning Language Models for Factuality**Katherine Tian, Eric Mitchell, Huaxiu Yao, et al.*2023 · 120 citations*](https://md.research.elamur.ai/free/en/papers/tian-2023-fine-tuning-language-models-factuality/)
138. [**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/)
139. [**ExpertQA: Expert-Curated Questions and Attributed Answers**Chaitanya Malaviya, Subin Lee, Sihao Chen, et al.*2024 · 109 citations*](https://md.research.elamur.ai/free/en/papers/malaviya-2024-expertqa-expert-curated-questions-attributed/)
140. [**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/)
141. [**Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation**Patrick Fernandes, Aman Madaan, Emmy Liu, et al.*2023 · 84 citations*](https://md.research.elamur.ai/free/en/papers/fernandes-2023-bridging-gap-survey-integrating-human/)
142. [**Coreference Resolution through a seq2seq Transition-Based System**Bernd Bohnet, Chris Alberti, Michael Collins*2023 · 77 citations*](https://md.research.elamur.ai/free/en/papers/bohnet-2023-coreference-resolution-through-seq2seq-transition/)
143. [**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/)
144. [**Transcending Scaling Laws with 0.1% Extra Compute**Yi Tay, Jason Wei, Hyung Won Chung, et al.*2022 · 40 citations*](https://md.research.elamur.ai/free/en/papers/tay-2022-transcending-scaling-laws-0-1/)
145. [**Exploring the Numerical Reasoning Capabilities of Language Models: A Comprehensive Analysis on Tabular Data**Mubashara Akhtar, Abhilash Shankarampeta, Vivek Gupta, et al.*2023 · 38 citations*](https://md.research.elamur.ai/free/en/papers/akhtar-2023-exploring-numerical-reasoning-capabilities-language/)
146. [**Reflect, Not Reflex: Inference-Based Common Ground Improves Dialogue Response Quality**Pei Zhou, Hyundong Cho, Pegah Jandaghi, et al.*2022 · 34 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2022-reflect-not-reflex-inference-based/)
147. [**The Gricean Maxims in NLP — A Survey**Lea Krause, Piek T.J.M. Vossen*2024 · 10 citations*](https://md.research.elamur.ai/free/en/papers/krause-2024-gricean-maxims-nlp-survey/)
148. [**HeSum: a Novel Dataset for Abstractive Text Summarization in Hebrew**Tzuf Paz-Argaman, Itai Mondshine, Asaf Achi Mordechai, et al.*2024 · 7 citations*](https://md.research.elamur.ai/free/en/papers/argaman-2024-hesum-novel-dataset-abstractive-text/)
149. [**Optimizing RAG Pipelines for Arabic: A Systematic Analysis of Core Components**Jumana Alsubhi, Mohammad D. Alahmadi, Ahmed Alhusayni, et al.*2025 · 2 citations*](https://md.research.elamur.ai/free/en/papers/alsubhi-2025-optimizing-rag-pipelines-arabic-systematic/)
150. [**Efficient Topic Extraction via Graph-Based Labeling: A Lightweight Alternative to Deep Models**Salma Mekaoui, Hiba Sofyan, Imane Benchrif, et al.*2026*](https://md.research.elamur.ai/free/en/papers/mekaoui-2026-efficient-topic-extraction-graph-based/)
151. [**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/)

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

1,056 more citing papers of our library have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/authors/vincent-zhao/) — Vincent Zhao