---
title: "Who cites Sasha Luccioni — page 2 — 2,113 citing papers"
description: "2,113 papers of the Elamur Research library cite the works of Sasha Luccioni, found in their bibliographies · page 2 of 6 · 101–200; the most…"
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---
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# Who cites Sasha Luccioni

2,303 citations of their works from 2,113 papers of our library (of the 149,439 whose bibliographies we read) · 508 with a page here, the most cited first · page 2 of 6 · 101–200 · [← Sasha Luccioni](https://md.research.elamur.ai/free/en/authors/sasha-luccioni/)

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101. **[A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-safety-trustworthiness-large-language/) [Artificial Intelligence Review](https://md.research.elamur.ai/free/en/venues/artificial-intelligence-review/)**Xiaowei Huang, Wenjie Ruan, Wei Huang, et al.*2023 · 199 citations*
102. [**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/)
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. [**DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence arXiv:2406.11931**DeepSeek-AI, Qihao Zhu, Daya Guo, et al.*2024 · 196 citations*](https://md.research.elamur.ai/free/en/papers/deepseek-ai-2024-deepseek-coder-v2-breaking-barrier/)
105. **[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*
106. [**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/)
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. [**CodeS: Towards Building Open-source Language Models for Text-to-SQL arXiv:2402.16347**Haoyang Li, Jing Zhang, Hanbing Liu, et al.*2024 · 190 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-codes-towards-building-open-source/)
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. **[TABBIE: Pretrained Representations of Tabular Data](https://md.research.elamur.ai/free/en/papers/iida-2021-tabbie-pretrained-representations-tabular-data/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Hiroshi Iida, Dung Thai, Varun Manjunatha, et al.*2021 · 186 citations*
113. **[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*
114. **[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*
115. **[Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation](https://md.research.elamur.ai/free/en/papers/singh-2025-global-mmlu-understanding-addressing-cultural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shivalika Singh, Angelika Romanou, Clémentine Fourrier, et al.*2025 · 182 citations*
116. **[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*
117. [**SmolLM2: When Smol Goes Big — Data-Centric Training of a Small Language Model arXiv:2502.02737**Loubna Ben Allal, Anton Lozhkov, Elie Bakouch, et al.*2025 · 181 citations*](https://md.research.elamur.ai/free/en/papers/allal-2025-smollm2-smol-goes-big-data/)
118. **[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*
119. **[On the Risk of Misinformation Pollution with Large Language Models](https://md.research.elamur.ai/free/en/papers/pan-2023-risk-misinformation-pollution-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yikang Pan, Liangming Pan, Wenhu Chen, et al.*2023 · 179 citations*
120. [**KernelBench: Can LLMs Write Efficient GPU Kernels? arXiv:2502.10517**Anne Ouyang, Simon Guo, Simran Arora, et al.*2025 · 178 citations*](https://md.research.elamur.ai/free/en/papers/ouyang-2025-kernelbench-llms-write-efficient-gpu/)
121. **[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*
122. [**BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions arXiv:2406.15877**Terry Yue Zhuo, Minh Chien Vu, Jenny Chim, et al.*2024 · 176 citations*](https://md.research.elamur.ai/free/en/papers/zhuo-2024-bigcodebench-benchmarking-code-generation-diverse/)
123. [**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/)
124. [**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/)
125. **[Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs](https://md.research.elamur.ai/free/en/papers/balloccu-2024-leak-cheat-repeat-data-contamination/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Simone Balloccu, Patrícia Schmidtová, Mateusz Lango, et al.*2024 · 174 citations*
126. [**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/)
127. **[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*
128. [**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/)
129. **[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*
130. **[LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers](https://md.research.elamur.ai/free/en/papers/olausson-2023-linc-neurosymbolic-approach-logical-reasoning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Theo X. Olausson, Alex Gu, Benjamin Lipkin, et al.*2023 · 172 citations*
131. [**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/)
132. **[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*
133. [**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/)
134. [**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/)
135. **[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*
136. [**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/)
137. **[Impact of Pretraining Term Frequencies on Few-Shot Numerical Reasoning](https://md.research.elamur.ai/free/en/papers/razeghi-2022-impact-pretraining-term-frequencies-few/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yasaman Razeghi, Robert L Logan IV, Matt Gardner, et al.*2022 · 165 citations*
138. **[AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages](https://md.research.elamur.ai/free/en/papers/ebrahimi-2022-americasnli-evaluating-zero-shot-natural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Abteen Ebrahimi, Manuel Mager, Arturo Oncevay, et al.*2022 · 164 citations*
139. [**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/)
140. [**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/)
141. **[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*
142. **[TheoremQA: A Theorem-driven Question Answering Dataset](https://md.research.elamur.ai/free/en/papers/chen-2023-theoremqa-theorem-driven-question-answering/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Wenhu Chen, Ming Yin, Max Ku, et al.*2023 · 158 citations*
143. [**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/)
144. **[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*
145. **[Probing Pre-Trained Language Models for Cross-Cultural Differences in Values](https://md.research.elamur.ai/free/en/papers/arora-2023-probing-pre-trained-language-models/) [Workshop on Cross-Cultural Considerations in NLP](https://md.research.elamur.ai/free/en/venues/c3nlp/)**Arnav Arora, Lucie-aimée Kaffee, Isabelle Augenstein*2023 · 156 citations*
146. **[Towards a Cleaner Document-Oriented Multilingual Crawled Corpus](https://md.research.elamur.ai/free/en/papers/abadji-2022-towards-cleaner-document-oriented-multilingual/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Julien Abadji, Pedro Ortiz Suarez, Laurent Romary, et al.*2022 · 156 citations*
147. **[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*
148. **[CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challenges](https://md.research.elamur.ai/free/en/papers/zhang-2024-codeagent-enhancing-code-generation-tool/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kechi Zhang, Jia Li, Ge Li, et al.*2024 · 150 citations*
149. **[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*
150. [**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/)
151. **[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*
152. [**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/)
153. **[Efficient Methods for Natural Language Processing: A Survey](https://md.research.elamur.ai/free/en/papers/treviso-2023-efficient-methods-natural-language-processing/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Marcos Treviso, Ji-Ung Lee, Tianchu Ji, et al.*2023 · 145 citations*
154. [**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/)
155. **[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*
156. **[Investigating Data Contamination in Modern Benchmarks for Large Language Models](https://md.research.elamur.ai/free/en/papers/deng-2024-investigating-data-contamination-modern-benchmarks/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Chunyuan Deng, Yilun Zhao, Xiangru Tang, et al.*2024 · 137 citations*
157. **[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*
158. **[Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset](https://md.research.elamur.ai/free/en/papers/henderson-2022-pile-law-learning-responsible-data/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Peter Henderson, Mark S. Krass, Lucia Zheng, et al.*2022 · 133 citations*
159. **[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*
160. **[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*
161. **[OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement](https://md.research.elamur.ai/free/en/papers/zheng-2024-opencodeinterpreter-integrating-code-generation-execution/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Tianyu Zheng, Ge Zhang, Tianhao Shen, et al.*2024 · 129 citations*
162. [**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/)
163. **[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*
164. [**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/)
165. **[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*
166. [**Debug like a Human: A Large Language Model Debugger via Verifying Runtime Execution Step-by-step arXiv:2402.16906**Li Zhong, Zilong Wang, Jingbo Shang*2024 · 124 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2024-debug-like-human-large-language/)
167. [**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/)
168. [**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/)
169. **[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*
170. [**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/)
171. [**CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution arXiv:2401.03065**Alex Gu, Baptiste Rozière, Hugh Leather, et al.*2024 · 122 citations*](https://md.research.elamur.ai/free/en/papers/gu-2024-cruxeval-benchmark-code-reasoning-understanding/)
172. [**RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems arXiv:2306.03091**Tianyang Liu, Canwen Xu, Julian McAuley*2023 · 122 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-repobench-benchmarking-repository-level-code/)
173. **[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*
174. [**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/)
175. [**LLM Self Defense: By Self Examination, LLMs Know They Are Being Tricked arXiv:2308.07308**Mansi Phute, Alec Helbling, Matthew Hull, et al.*2023 · 119 citations*](https://md.research.elamur.ai/free/en/papers/phute-2023-llm-self-defense-self-examination/)
176. [**RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation arXiv:2303.12570**Fengji Zhang, Bei Chen, Yue Zhang, et al.*2023 · 119 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-repocoder-repository-level-code-completion/)
177. [**Agentless: Demystifying LLM-based Software Engineering Agents arXiv:2407.01489**Chunqiu Steven Xia, Yinlin Deng, Soren Dunn, et al.*2024 · 117 citations*](https://md.research.elamur.ai/free/en/papers/xia-2024-agentless-demystifying-llm-based/)
178. **[ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic](https://md.research.elamur.ai/free/en/papers/koto-2024-arabicmmlu-assessing-massive-multitask-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Fajri Koto, Haonan Li, Sara Shatnawi, et al.*2024 · 117 citations*
179. [**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/)
180. [**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/)
181. [**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/)
182. [**ALLaM: Large Language Models for Arabic and English arXiv:2407.15390**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/)
183. [**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/)
184. [**Benchmarking Machine Translation with Cultural Awareness arXiv:2305.14328**Binwei Yao, Ming Jiang, Tara Bobinac, et al.*2023 · 110 citations*](https://md.research.elamur.ai/free/en/papers/yao-2023-benchmarking-machine-translation-cultural-awareness/)
185. [**Efficient Large Language Models: A Survey arXiv:2312.03863**Zhongwei Wan, Xin Wang, Che Liu, et al.*2023 · 110 citations*](https://md.research.elamur.ai/free/en/papers/wan-2023-efficient-large-language-models-survey/)
186. **[Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems](https://md.research.elamur.ai/free/en/papers/miao-2023-towards-efficient-generative-large-language/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Xupeng Miao, Gabriele Oliaro, Zhihao Zhang, et al.*2023 · 110 citations*
187. [**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/)
188. **[Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key?](https://md.research.elamur.ai/free/en/papers/wang-2024-rethinking-bounds-llm-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qineng Wang, Zihao Wang, Ying Su, et al.*2024 · 107 citations*
189. **[A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations](https://md.research.elamur.ai/free/en/papers/laskar-2024-systematic-survey-critical-review-evaluating/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Md Tahmid Rahman Laskar, Sawsan Alqahtani, M Saiful Bari, et al.*2024 · 105 citations*
190. **[Tokenizer Choice For LLM Training: Negligible or Crucial?](https://md.research.elamur.ai/free/en/papers/ali-2024-tokenizer-choice-llm-training-negligible/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mehdi Ali, Michael Fromm, Klaudia Thellmann, et al.*2024 · 104 citations*
191. [**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/)
192. [**MMTEB: Massive Multilingual Text Embedding Benchmark arXiv:2502.13595**Kenneth Enevoldsen, Isaac Chung, Imene Kerboua, et al.*2025 · 103 citations*](https://md.research.elamur.ai/free/en/papers/enevoldsen-2025-mmteb-massive-multilingual-text-embedding/)
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194. [**PolyLM: An Open Source Polyglot Large Language Model arXiv:2307.06018**Xiangpeng Wei, Haoran Wei, Huan Lin, et al.*2023 · 103 citations*](https://md.research.elamur.ai/free/en/papers/wei-2023-polylm-open-source-polyglot-large/)
195. [**Rethinking Benchmark and Contamination for Language Models with Rephrased Samples arXiv:2311.04850**Shuo Yang, Wei-Lin Chiang, Lianmin Zheng, et al.*2023 · 103 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-rethinking-benchmark-contamination-language-models/)
196. **[BLOOM+1: Adding Language Support to BLOOM for Zero-Shot Prompting](https://md.research.elamur.ai/free/en/papers/yong-2023-bloom-1-adding-language-support/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zheng Xin Yong, Hailey Schoelkopf, Niklas Muennighoff, et al.*2023 · 102 citations*
197. **[Do Multilingual Language Models Think Better in English?](https://md.research.elamur.ai/free/en/papers/etxaniz-2024-do-multilingual-language-models-think/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Julen Etxaniz, Gorka Azkune, Aitor Soroa, et al.*2024 · 101 citations*
198. [**AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Web arXiv:2305.13117**Michael Schlichtkrull, Zhijiang Guo, Andreas Vlachos*2023 · 101 citations*](https://md.research.elamur.ai/free/en/papers/schlichtkrull-2023-averitec-dataset-real-world-claim/)
199. [**FireAct: Toward Language Agent Fine-tuning arXiv:2310.05915**Baian Chen, Chang Shu, Ehsan Shareghi, et al.*2023 · 101 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-fireact-toward-language-agent-fine/)
200. [**FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets arXiv:2307.10928**Seonghyeon Ye, Doyoung Kim, Sungdong Kim, et al.*2023 · 100 citations*](https://md.research.elamur.ai/free/en/papers/ye-2023-flask-fine-grained-language-model/)

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