---
title: "Who cites Shaden Smith — page 2 — 1,715 papers of our library"
description: "1,715 papers of the Elamur Research library cite the works of Shaden Smith, found in their bibliographies · page 2 of 4 · 101–200; the most…"
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---
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# Who cites Shaden Smith

1,849 citations of their works from 1,715 papers of our library (of the 149,439 whose bibliographies we read) · 400 with a page here, the most cited first · page 2 of 4 · 101–200 · [← Shaden Smith](https://md.research.elamur.ai/free/en/authors/shaden-smith/)

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101. [**MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning arXiv:2310.09478**Jun Chen, Deyao Zhu, Xiaoqian Shen, et al.*2023 · 199 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-minigpt-v2-large-language-model/)
102. **[Improving Passage Retrieval with Zero-Shot Question Generation](https://md.research.elamur.ai/free/en/papers/sachan-2022-improving-passage-retrieval-zero-shot/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Devendra Sachan, Mike Lewis, Mandar Joshi, et al.*2022 · 199 citations*
103. **[OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents](https://md.research.elamur.ai/free/en/papers/lauren-ccon-2023-obelics-open-web-scale-filtered/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Hugo Laurençon, Lucile Saulnier, Leo Tronchon, et al.*2023 · 197 citations*
104. **[Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback](https://md.research.elamur.ai/free/en/papers/lai-2023-okapi-instruction-tuned-large-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Viet Lai, Chien Nguyen, Nghia Ngo, et al.*2023 · 195 citations*
105. [**PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization arXiv:2306.05087**Yidong Wang, Zhuohao Yu, Zhengran Zeng, et al.*2023 · 194 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-pandalm-automatic-evaluation-benchmark-llm/)
106. **[LLM in a flash: Efficient Large Language Model Inference with Limited Memory](https://md.research.elamur.ai/free/en/papers/alizadeh-2024-llm-flash-efficient-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Keivan Alizadeh, Seyed Iman Mirzadeh, Dmitry Belenko, et al.*2024 · 193 citations*
107. **[On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning](https://md.research.elamur.ai/free/en/papers/shaikh-2023-second-thought-let-s-not/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Omar Shaikh, Hongxin Zhang, William Held, et al.*2023 · 192 citations*
108. **[DataComp-LM: In search of the next generation of training sets for language models](https://md.research.elamur.ai/free/en/papers/li-2024-datacomp-lm-search-next-generation/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jeffrey Li, Alex Fang, Georgios Smyrnis, et al.*2024 · 191 citations*
109. **[Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models](https://md.research.elamur.ai/free/en/papers/tirumala-2022-memorization-overfitting-analyzing-training-dynamics/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Kushal Tirumala, Aram H. Markosyan, Luke Zettlemoyer, et al.*2022 · 190 citations*
110. [**Eliciting Latent Predictions from Transformers with the Tuned Lens arXiv:2303.08112**Nora Belrose, Igor Ostrovsky, Lev McKinney, et al.*2023 · 188 citations*](https://md.research.elamur.ai/free/en/papers/belrose-2023-eliciting-latent-predictions-transformers-tuned/)
111. [**Adaptive Machine Translation with Large Language Models arXiv:2301.13294**Yasmin Moslem, Rejwanul Haque, John D. Kelleher, et al.*2023 · 186 citations*](https://md.research.elamur.ai/free/en/papers/moslem-2023-adaptive-machine-translation-large-language/)
112. **[Towards Building Multilingual Language Model for Medicine](https://md.research.elamur.ai/free/en/papers/qiu-2024-towards-building-multilingual-language-model/) [Nature Communications](https://md.research.elamur.ai/free/en/venues/nature-communications/)**Pengcheng Qiu, Chaoyi Wu, Xiaoman Zhang, et al.*2024 · 184 citations*
113. **[GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models](https://md.research.elamur.ai/free/en/papers/prasad-2023-grips-gradient-free-edit-based/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Archiki Prasad, Peter Hase, Xiang Zhou, et al.*2023 · 184 citations*
114. **[Large Language Models are few(1)-shot Table Reasoners](https://md.research.elamur.ai/free/en/papers/chen-2023-large-language-models-are-few/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wenhu Chen*2023 · 182 citations*
115. **[ToolQA: A Dataset for LLM Question Answering with External Tools](https://md.research.elamur.ai/free/en/papers/zhuang-2023-toolqa-dataset-llm-question-answering/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yuchen Zhuang, Yue Yu, Kuan Wang, et al.*2023 · 182 citations*
116. **[CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages](https://md.research.elamur.ai/free/en/papers/nguyen-2023-culturax-cleaned-enormous-multilingual-dataset/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Thuat Nguyen, Chien Van Nguyen, Viet Dac Lai, et al.*2023 · 181 citations*
117. [**8-bit Optimizers via Block-wise Quantization arXiv:2110.02861**Tim Dettmers, Mike Lewis, Sam Shleifer, et al.*2021 · 181 citations*](https://md.research.elamur.ai/free/en/papers/dettmers-2021-8-bit-optimizers-block-wise/)
118. **[Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Again](https://md.research.elamur.ai/free/en/papers/gutierrez-2022-thinking-about-gpt-3-context/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Bernal Jimenez Gutierrez, Nikolas McNeal, Clayton Washington, et al.*2022 · 180 citations*
119. **[Do Llamas Work in English? On the Latent Language of Multilingual Transformers](https://md.research.elamur.ai/free/en/papers/wendler-2024-do-llamas-work-english-latent/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chris Wendler, Veniamin Veselovsky, Giovanni Monea, et al.*2024 · 177 citations*
120. [**Aligning Large Multimodal Models with Factually Augmented RLHF arXiv:2309.14525**Zhiqing Sun, Sheng Shen, Shengcao Cao, et al.*2023 · 176 citations*](https://md.research.elamur.ai/free/en/papers/sun-2023-aligning-large-multimodal-models-factually/)
121. [**On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective arXiv:2302.12095**Jindong Wang, Xixu Hu, Wenxin Hou, et al.*2023 · 176 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-robustness-chatgpt-adversarial-out-distribution/)
122. [**Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents arXiv:2302.01560**Zihao Wang, Shaofei Cai, Guanzhou Chen, et al.*2023 · 174 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-describe-explain-plan-select-interactive/)
123. **[Do All Languages Cost the Same? Tokenization in the Era of Commercial Language Models](https://md.research.elamur.ai/free/en/papers/ahia-2023-do-all-languages-cost-same/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Orevaoghene Ahia, Sachin Kumar, Hila Gonen, et al.*2023 · 174 citations*
124. [**Generative Representational Instruction Tuning arXiv:2402.09906**Niklas Muennighoff, Hongjin Su, Liang Wang, et al.*2024 · 172 citations*](https://md.research.elamur.ai/free/en/papers/muennighoff-2024-generative-representational-instruction-tuning/)
125. **[DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text](https://md.research.elamur.ai/free/en/papers/su-2023-detectllm-leveraging-log-rank-information/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jinyan Su, Terry Zhuo, Di Wang, et al.*2023 · 172 citations*
126. [**Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity arXiv:2310.07521**Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, et al.*2023 · 171 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-survey-factuality-large-language-models/)
127. **[The Troubling Emergence of Hallucination in Large Language Models - An Extensive Definition, Quantification, and Prescriptive Remediations](https://md.research.elamur.ai/free/en/papers/rawte-2023-troubling-emergence-hallucination-large-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Vipula Rawte, Swagata Chakraborty, Agnibh Pathak, et al.*2023 · 171 citations*
128. [**Capabilities of Gemini Models in Medicine arXiv:2404.18416**Khaled Saab, Tao Tu, Wei-Hung Weng, et al.*2024 · 170 citations*](https://md.research.elamur.ai/free/en/papers/saab-2024-capabilities-gemini-models-medicine/)
129. [**SqueezeLLM: Dense-and-Sparse Quantization arXiv:2306.07629**Sehoon Kim, Coleman Hooper, Amir Gholami, et al.*2023 · 170 citations*](https://md.research.elamur.ai/free/en/papers/kim-2023-squeezellm-dense-sparse-quantization/)
130. **[Speak, Memory: An Archaeology of Books Known to ChatGPT/GPT-4](https://md.research.elamur.ai/free/en/papers/chang-2023-speak-memory-archaeology-books-known/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Kent K. Chang, Mackenzie Cramer, Sandeep Soni, et al.*2023 · 169 citations*
131. **[Factuality Enhanced Language Models for Open-Ended Text Generation](https://md.research.elamur.ai/free/en/papers/lee-2022-factuality-enhanced-language-models-open/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Nayeon Lee, Wei Ping, Peng Xu, et al.*2022 · 168 citations*
132. [**SantaCoder: don't reach for the stars! arXiv:2301.03988**Loubna Ben Allal, Raymond Li, Denis Kocetkov, et al.*2023 · 167 citations*](https://md.research.elamur.ai/free/en/papers/allal-2023-santacoder-don-t-reach-stars/)
133. [**DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models arXiv:2309.14509**Sam Ade Jacobs, Masahiro Tanaka, Chengming Zhang, et al.*2023 · 162 citations*](https://md.research.elamur.ai/free/en/papers/jacobs-2023-deepspeed-ulysses-system-optimizations-enabling/)
134. [**ChatGPT as a Factual Inconsistency Evaluator for Text Summarization arXiv:2303.15621**Zheheng Luo, Qianqian Xie, Sophia Ananiadou*2023 · 161 citations*](https://md.research.elamur.ai/free/en/papers/luo-2023-chatgpt-factual-inconsistency-evaluator-text/)
135. [**From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning arXiv:2308.12032**Ming Li, Yong Zhang, Zhitao Li, et al.*2023 · 161 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-quantity-quality-boosting-llm-performance/)
136. **[A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness](https://md.research.elamur.ai/free/en/papers/wang-2024-comprehensive-survey-small-language-models/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Fali Wang, Zhiwei Zhang, Xianren Zhang, et al.*2024 · 159 citations*
137. **[Large Language Models with Controllable Working Memory](https://md.research.elamur.ai/free/en/papers/li-2023-large-language-models-controllable-working/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Daliang Li, Ankit Singh Rawat, Manzil Zaheer, et al.*2023 · 158 citations*
138. [**MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs arXiv:2402.15627**Ziheng Jiang, Haibin Lin, Yinmin Zhong, et al.*2024 · 157 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2024-megascale-scaling-large-language-model/)
139. [**Leveraging Large Language Models for Multiple Choice Question Answering arXiv:2210.12353**Joshua Robinson, Christopher Michael Rytting, David Wingate*2022 · 157 citations*](https://md.research.elamur.ai/free/en/papers/robinson-2022-leveraging-large-language-models-multiple/)
140. **[Language-Specific Neurons: The Key to Multilingual Capabilities in Large Language Models](https://md.research.elamur.ai/free/en/papers/tang-2024-language-specific-neurons-key-multilingual/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tianyi Tang, Wenyang Luo, Haoyang Huang, et al.*2024 · 156 citations*
141. **[ChatGPT MT: Competitive for High- (but Not Low-) Resource Languages](https://md.research.elamur.ai/free/en/papers/robinson-2023-chatgpt-mt-competitive-high-but/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Nathaniel Robinson, Perez Ogayo, David R. Mortensen, et al.*2023 · 153 citations*
142. **[M3Exam: A Multilingual, Multimodal, Multilevel Benchmark for Examining Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2023-m3exam-multilingual-multimodal-multilevel-benchmark/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Wenxuan Zhang, Sharifah Mahani Aljunied, Chang Gao, et al.*2023 · 150 citations*
143. [**The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset arXiv:2303.03915**Hugo Laurençon, Lucile Saulnier, Thomas Wang, et al.*2023 · 150 citations*](https://md.research.elamur.ai/free/en/papers/lauren-ccon-2023-bigscience-roots-corpus-1-6tb/)
144. **[OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models](https://md.research.elamur.ai/free/en/papers/lee-2023-owq-outlier-aware-weight-quantization/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Changhun Lee, Jungyu Jin, Taesu Kim, et al.*2023 · 149 citations*
145. [**Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature arXiv:2310.05130**Guangsheng Bao, Yanbin Zhao, Zhiyang Teng, et al.*2023 · 148 citations*](https://md.research.elamur.ai/free/en/papers/bao-2023-fast-detectgpt-efficient-zero-shot/)
146. **[Full Parameter Fine-tuning for Large Language Models with Limited Resources](https://md.research.elamur.ai/free/en/papers/lv-2024-full-parameter-fine-tuning-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kai Lv, Yuqing Yang, Tengxiao Liu, et al.*2024 · 143 citations*
147. [**A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generation arXiv:2307.03987**Neeraj Varshney; Wenlin Yao; Hongming Zhang; Jianshu Chen;, Dong Yu*2023 · 143 citations*](https://md.research.elamur.ai/free/en/papers/varshney-2023-stitch-time-saves-nine-detecting/)
148. [**Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness arXiv:2304.11633**Bo Li, Gexiang Fang, Yang Yang, et al.*2023 · 142 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-evaluating-chatgpt-s-information-extraction/)
149. **[DRAGIN: Dynamic Retrieval Augmented Generation based on the Real-time Information Needs of Large Language Models](https://md.research.elamur.ai/free/en/papers/su-2024-dragin-dynamic-retrieval-augmented-generation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Weihang Su, Yichen Tang, Qingyao Ai, et al.*2024 · 137 citations*
150. **[AdaMix: Mixture-of-Adaptations for Parameter-efficient Model Tuning](https://md.research.elamur.ai/free/en/papers/wang-2022-adamix-mixture-adaptations-parameter-efficient/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yaqing Wang, Sahaj Agarwal, Subhabrata Mukherjee, et al.*2022 · 135 citations*
151. **[The Impact of Positional Encoding on Length Generalization in Transformers](https://md.research.elamur.ai/free/en/papers/kazemnejad-2023-impact-positional-encoding-length-generalization/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Amirhossein Kazemnejad, Inkit Padhi, Karthikeyan Natesan Ramamurthy, et al.*2023 · 134 citations*
152. **[Deductive Verification of Chain-of-Thought Reasoning](https://md.research.elamur.ai/free/en/papers/ling-2023-deductive-verification-chain-thought-reasoning/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhan Ling, Yunhao Fang, Xuanlin Li, et al.*2023 · 130 citations*
153. [**Scaling Up Models and Data with t5x and seqio arXiv:2203.17189**Adam Roberts, Hyung Won Chung, Anselm Levskaya, et al.*2022 · 130 citations*](https://md.research.elamur.ai/free/en/papers/roberts-2022-scaling-up-models-data-texttt/)
154. **[You reap what you sow: On the Challenges of Bias Evaluation Under Multilingual Settings](https://md.research.elamur.ai/free/en/papers/talat-2022-you-reap-what-you-sow/) [BigScience Episode – Workshop on Challenges & Perspectives in Creating Large Language Models](https://md.research.elamur.ai/free/en/venues/bigscience/)**Zeerak Talat, Aurélie Névéol, Stella Biderman, et al.*2022 · 130 citations*
155. **[MAGE: Machine-generated Text Detection in the Wild](https://md.research.elamur.ai/free/en/papers/li-2024-mage-machine-generated-text-detection/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yafu Li, Qintong Li, Leyang Cui, et al.*2024 · 129 citations*
156. [**Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning arXiv:2303.15647**Vladislav Lialin, Vijeta Deshpande, Xiaowei Yao, et al.*2023 · 129 citations*](https://md.research.elamur.ai/free/en/papers/lialin-2023-scaling-down-scale-up-guide/)
157. **[Sequence Parallelism: Long Sequence Training from System Perspective](https://md.research.elamur.ai/free/en/papers/li-2023-sequence-parallelism-long-sequence-training/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shenggui Li, Fuzhao Xue, Chaitanya Baranwal, et al.*2023 · 129 citations*
158. **[Scaling Sentence Embeddings with Large Language Models](https://md.research.elamur.ai/free/en/papers/jiang-2024-scaling-sentence-embeddings-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ting Jiang, Shaohan Huang, Zhongzhi Luan, et al.*2024 · 127 citations*
159. [**Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future arXiv:2309.15402**Zheng Chu, Jingchang Chen, Qianglong Chen, et al.*2023 · 125 citations*](https://md.research.elamur.ai/free/en/papers/chu-2023-navigate-through-enigmatic-labyrinth-survey/)
160. **[XuanYuan 2.0: A Large Chinese Financial Chat Model with Hundreds of Billions Parameters](https://md.research.elamur.ai/free/en/papers/zhang-2023-xuanyuan-2-0-large-chinese/) [ACM International Conference on Information and Knowledge Management (CIKM)](https://md.research.elamur.ai/free/en/venues/acm-international-conference-on-information-and-knowledge-management-cikm/)**Xuanyu Zhang, Qing Yang, Dongliang Xu*2023 · 125 citations*
161. [**H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models arXiv:2306.14048**Zhenyu Zhang, Ying Sheng, Tianyi Zhou, et al.*2023 · 124 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-h-2-o-heavy-hitter/)
162. [**Large Language Model Alignment: A Survey arXiv:2309.15025**Tianhao Shen, Renren Jin, Yufei Huang, et al.*2023 · 124 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-large-language-model-alignment-survey/)
163. **[Unlearn What You Want to Forget: Efficient Unlearning for LLMs](https://md.research.elamur.ai/free/en/papers/chen-2023-unlearn-what-you-want-forget/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jiaao Chen, Diyi Yang*2023 · 124 citations*
164. **[Emergent and Predictable Memorization in Large Language Models](https://md.research.elamur.ai/free/en/papers/biderman-2023-emergent-predictable-memorization-large-language/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Stella Biderman, USVSN Sai Prashanth, Lintang Sutawika, et al.*2023 · 123 citations*
165. [**The case for 4-bit precision: k-bit Inference Scaling Laws arXiv:2212.09720**Tim Dettmers, Luke Zettlemoyer*2022 · 123 citations*](https://md.research.elamur.ai/free/en/papers/dettmers-2022-case-4-bit-precision-k/)
166. **[AlphaMath Almost Zero: Process Supervision without Process](https://md.research.elamur.ai/free/en/papers/chen-2024-alphamath-almost-zero-process-supervision/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Guoxin Chen, Minpeng Liao, Chengxi Li, et al.*2024 · 120 citations*
167. **[XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language Models](https://md.research.elamur.ai/free/en/papers/liang-2023-xlm-v-overcoming-vocabulary-bottleneck/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Davis Liang, Hila Gonen, Yuning Mao, et al.*2023 · 120 citations*
168. [**SaulLM-7B: A pioneering Large Language Model for Law arXiv:2403.03883**Pierre Colombo, Telmo Pessoa Pires, Malik Boudiaf, et al.*2024 · 119 citations*](https://md.research.elamur.ai/free/en/papers/colombo-2024-saullm-7b-pioneering-large-language/)
169. [**Large Language Models Understand and Can be Enhanced by Emotional Stimuli arXiv:2307.11760**Cheng Li, Jindong Wang, Yixuan Zhang, et al.*2023 · 116 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-large-language-models-understand-enhanced/)
170. [**PandaGPT: One Model To Instruction-Follow Them All arXiv:2305.16355**Yixuan Su, Tian Lan, Huayang Li, et al.*2023 · 115 citations*](https://md.research.elamur.ai/free/en/papers/su-2023-pandagpt-one-model-instruction-follow/)
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172. [**A Survey on Efficient Inference for Large Language Models arXiv:2404.14294**Zixuan Zhou, Xuefei Ning, Ke Hong, et al.*2024 · 113 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2024-survey-efficient-inference-large-language/)
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174. [**Efficient Large Scale Language Modeling with Mixtures of Experts arXiv:2112.10684**Mikel Artetxe, Shruti Bhosale, Naman Goyal, et al.*2021 · 113 citations*](https://md.research.elamur.ai/free/en/papers/artetxe-2021-efficient-large-scale-language-modeling/)
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178. [**Prompt Cache: Modular Attention Reuse for Low-Latency Inference MLSys 2024**In Gim, Guojun Chen, Seung-seob Lee, et al.*2023 · 109 citations*](https://md.research.elamur.ai/free/en/papers/gim-2023-prompt-cache-modular-attention-reuse/)
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182. [**A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity arXiv:2305.13169**Shayne Longpre, Gregory Yauney, Emily Reif, et al.*2023 · 104 citations*](https://md.research.elamur.ai/free/en/papers/longpre-2023-pretrainer-s-guide-training-data/)
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196. [**Bactrian-X: Multilingual Replicable Instruction-Following Models with Low-Rank Adaptation arXiv:2305.15011**Haonan Li, Fajri Koto, Minghao Wu, et al.*2023 · 96 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-bactrian-x-multilingual-replicable-instruction/)
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