---
title: "Who cites Benoit Steiner — page 2 — 4,980 papers of our library"
description: "4,980 papers of the Elamur Research library cite the works of Benoit Steiner, found in their bibliographies · page 2 of 5 · 101–200; the most…"
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---
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# Who cites Benoit Steiner

5,349 citations of their works from 4,980 papers of our library (of the 149,439 whose bibliographies we read) · 473 with a page here, the most cited first · page 2 of 5 · 101–200 · [← Benoit Steiner](https://md.research.elamur.ai/free/en/authors/benoit-steiner/)

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101. [**Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning arXiv:2108.10470**Viktor Makoviychuk, Lukasz Wawrzyniak, Yunrong Guo, et al.*2021 · 209 citations*](https://md.research.elamur.ai/free/en/papers/makoviychuk-2021-isaac-gym-high-performance-gpu/)
102. **[ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Search](https://md.research.elamur.ai/free/en/papers/zhang-2024-rest-mcts-llm-self-training/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Dan Zhang, Sining Zhoubian, Ziniu Hu, et al.*2024 · 200 citations*
103. **[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*
104. [**Refusal in Language Models Is Mediated by a Single Direction arXiv:2406.11717**Andy Arditi, Oscar Obeso, Aaquib Syed, et al.*2024 · 196 citations*](https://md.research.elamur.ai/free/en/papers/arditi-2024-refusal-language-models-mediated-single/)
105. **[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*
106. **[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*
107. **[LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations](https://md.research.elamur.ai/free/en/papers/cao-2021-lgesql-line-graph-enhanced-text/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ruisheng Cao, Lu Chen, Zhi Chen, et al.*2021 · 191 citations*
108. **[Tensor2Tensor for Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/vaswani-2018-tensor2tensor-neural-machine-translation/) [Conference of the Association for Machine Translation in the Americas (AMTA)](https://md.research.elamur.ai/free/en/venues/amta/)**Ashish Vaswani, Samy Bengio, Eugene Brevdo, et al.*2018 · 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. [**End-to-End Differentiable Proving arXiv:1705.11040**Tim Rocktäschel, Sebastian Riedel*2017 · 188 citations*](https://md.research.elamur.ai/free/en/papers/rocktaschel-2017-end-end-differentiable-proving/)
111. **[Knowledge Conflicts for LLMs: A Survey](https://md.research.elamur.ai/free/en/papers/xu-2024-knowledge-conflicts-llms-survey/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Rongwu Xu, Zehan Qi, Zhijiang Guo, et al.*2024 · 182 citations*
112. **[Modeling Intra and Inter-modality Incongruity for Multi-Modal Sarcasm Detection](https://md.research.elamur.ai/free/en/papers/pan-2020-modeling-intra-inter-modality-incongruity/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hongliang Pan, Zheng Lin, Peng Fu, et al.*2020 · 182 citations*
113. [**Open Question Answering over Tables and Text arXiv:2010.10439**Wenhu Chen, Ming-Wei Chang, Eva Schlinger, et al.*2020 · 182 citations*](https://md.research.elamur.ai/free/en/papers/chen-2020-open-question-answering-over-tables/)
114. **[Prompt-learning for Fine-grained Entity Typing](https://md.research.elamur.ai/free/en/papers/ding-2022-prompt-learning-fine-grained-entity/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ning Ding, Yulin Chen, Xu Han, et al.*2022 · 181 citations*
115. [**Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads arXiv:1901.05758**Myeongjae Jeon, Shivaram Venkataraman, Amar Phanishayee, et al.*2019 · 179 citations*](https://md.research.elamur.ai/free/en/papers/jeon-2019-analysis-large-scale-multi-tenant/)
116. [**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/)
117. [**LESS: Selecting Influential Data for Targeted Instruction Tuning arXiv:2402.04333**Mengzhou Xia, Sadhika Malladi, Suchin Gururangan, et al.*2024 · 178 citations*](https://md.research.elamur.ai/free/en/papers/xia-2024-less-selecting-influential-data-targeted/)
118. **[Learning to Compress Prompts with Gist Tokens](https://md.research.elamur.ai/free/en/papers/mu-2023-learning-compress-prompts-gist-tokens/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jesse Mu, Xiang Lisa Li, Noah Goodman*2023 · 178 citations*
119. **[Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP](https://md.research.elamur.ai/free/en/papers/goot-2021-massive-choice-ample-tasks-machamp/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Rob van der Goot, Ahmet Üstün, Alan Ramponi, et al.*2021 · 177 citations*
120. [**AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning arXiv:2303.10512**Qingru Zhang, Minshuo Chen, Alexander Bukharin, et al.*2023 · 173 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-adalora-adaptive-budget-allocation-parameter/)
121. [**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/)
122. [**Tutel: Adaptive Mixture-of-Experts at Scale MLSys 2023**Changho Hwang, Wei Cui, Yifan Xiong, et al.*2022 · 168 citations*](https://md.research.elamur.ai/free/en/papers/hwang-2022-tutel-adaptive-mixture-experts-scale/)
123. **[Membership Inference Attacks against Language Models via Neighbourhood Comparison](https://md.research.elamur.ai/free/en/papers/mattern-2023-membership-inference-attacks-against-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Justus Mattern, Fatemehsadat Mireshghallah, Zhijing Jin, et al.*2023 · 165 citations*
124. **[Improving Medical Reasoning through Retrieval and Self-Reflection with Retrieval-Augmented Large Language Models](https://md.research.elamur.ai/free/en/papers/jeong-2024-improving-medical-reasoning-through-retrieval/) [Bioinformatics](https://md.research.elamur.ai/free/en/venues/bioinformatics/)**Minbyul Jeong, Jiwoong Sohn, Mujeen Sung, et al.*2024 · 164 citations*
125. **[Towards Persona-Based Empathetic Conversational Models](https://md.research.elamur.ai/free/en/papers/zhong-2020-persona-based-empathetic-conversational-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Peixiang Zhong, Chen Zhang, Hao Wang, et al.*2020 · 162 citations*
126. [**PipeDream: Fast and Efficient Pipeline Parallel DNN Training arXiv:1806.03377**Aaron Harlap, Deepak Narayanan, Amar Phanishayee, et al.*2018 · 162 citations*](https://md.research.elamur.ai/free/en/papers/harlap-2018-pipedream-fast-efficient-pipeline-parallel/)
127. [**Unsupervised Text Style Transfer using Language Models as Discriminators arXiv:1805.11749**Zichao Yang, Zhiting Hu, Chris Dyer, et al.*2018 · 161 citations*](https://md.research.elamur.ai/free/en/papers/yang-2018-unsupervised-text-style-transfer-using/)
128. [**Chameleon: Mixed-Modal Early-Fusion Foundation Models arXiv:2405.09818**Chameleon Team*2024 · 159 citations*](https://md.research.elamur.ai/free/en/papers/team-2024-chameleon-mixed-modal-early-fusion/)
129. [**CLIPort: What and Where Pathways for Robotic Manipulation arXiv:2109.12098**Mohit Shridhar, Lucas Manuelli, Dieter Fox*2021 · 159 citations*](https://md.research.elamur.ai/free/en/papers/shridhar-2021-cliport-where-pathways-robotic-manipulation/)
130. [**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/)
131. **[Reducing Quantity Hallucinations in Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/zhao-2020-reducing-quantity-hallucinations-abstractive-summarization/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zheng Zhao, Shay B. Cohen, Bonnie Webber*2020 · 157 citations*
132. **[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*
133. **[Effective Domain Mixing for Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/britz-2017-effective-domain-mixing-neural-machine/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Denny Britz, Quoc Le, Reid Pryzant*2017 · 156 citations*
134. **[Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts](https://md.research.elamur.ai/free/en/papers/wang-2024-interpretable-preferences-multi-objective-reward/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Haoxiang Wang, Wei Xiong, Tengyang Xie, et al.*2024 · 153 citations*
135. **[Fine-Tuning Language Models with Just Forward Passes](https://md.research.elamur.ai/free/en/papers/malladi-2023-fine-tuning-language-models-just/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Sadhika Malladi, Tianyu Gao, Eshaan Nichani, et al.*2023 · 153 citations*
136. **[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*
137. [**MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge arXiv:2206.08853**Linxi Fan, Guanzhi Wang, Yunfan Jiang, et al.*2022 · 149 citations*](https://md.research.elamur.ai/free/en/papers/fan-2022-minedojo-building-open-ended-embodied/)
138. [**MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation arXiv:2310.03302**Qian Huang, Jian Vora, Percy Liang, et al.*2023 · 147 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-mlagentbench-evaluating-language-agents/)
139. [**rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking arXiv:2501.04519**Xinyu Guan, Li Lyna Zhang, Yifei Liu, et al.*2025 · 146 citations*](https://md.research.elamur.ai/free/en/papers/guan-2025-rstar-math-small-llms-can/)
140. **[Word-Level Coreference Resolution](https://md.research.elamur.ai/free/en/papers/dobrovolskii-2021-word-level-coreference-resolution/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Vladimir Dobrovolskii*2021 · 146 citations*
141. **[SpotServe: Serving Generative Large Language Models on Preemptible Instances](https://md.research.elamur.ai/free/en/papers/miao-2023-spotserve-serving-generative-large-language/) [ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)](https://md.research.elamur.ai/free/en/venues/acm-international-conference-on-architectural-support-for-programming-languages/)**Xupeng Miao, Chunan Shi, Jiangfei Duan, et al.*2023 · 145 citations*
142. [**CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks arXiv:2105.12655**Ruchir Puri, David S. Kung, Geert Janssen, et al.*2021 · 141 citations*](https://md.research.elamur.ai/free/en/papers/puri-2021-codenet-large-scale-ai-code/)
143. **[Multimodal Pretraining Unmasked: A Meta-Analysis and a Unified Framework of Vision-and-Language BERTs](https://md.research.elamur.ai/free/en/papers/bugliarello-2021-multimodal-pretraining-unmasked-meta-analysis/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Emanuele Bugliarello, Ryan Cotterell, Naoaki Okazaki, et al.*2021 · 141 citations*
144. [**Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads arXiv:2008.09213**Deepak Narayanan, Keshav Santhanam, Fiodar Kazhamiaka, et al.*2020 · 141 citations*](https://md.research.elamur.ai/free/en/papers/narayanan-2020-heterogeneity-aware-cluster-scheduling-policies/)
145. **[Symbolic Chain-of-Thought Distillation: Small Models Can Also “Think” Step-by-Step](https://md.research.elamur.ai/free/en/papers/li-2023-symbolic-chain-thought-distillation-small/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Liunian Harold Li, Jack Hessel, Youngjae Yu, et al.*2023 · 140 citations*
146. **[LLaVA-CoT: Let Vision Language Models Reason Step-by-Step](https://md.research.elamur.ai/free/en/papers/xu-2024-llava-cot-let-vision-language/) [IEEE/CVF International Conference on Computer Vision (ICCV)](https://md.research.elamur.ai/free/en/venues/ieee-cvf-international-conference-on-computer-vision-iccv/)**Guowei Xu, Peng Jin, Ziang Wu, et al.*2024 · 138 citations*
147. [**To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning arXiv:2409.12183**Zayne Sprague, Fangcong Yin, Juan Diego Rodriguez, et al.*2024 · 138 citations*](https://md.research.elamur.ai/free/en/papers/sprague-2024-cot-cot-chain-thought-helps/)
148. **[Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics](https://md.research.elamur.ai/free/en/papers/bhargava-2021-generalization-nli-ways-not-go/) [Workshop on Insights from Negative Results in NLP](https://md.research.elamur.ai/free/en/venues/insights/)**Prajjwal Bhargava, Aleksandr Drozd, Anna Rogers*2021 · 131 citations*
149. [**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/)
150. [**Learning to Represent Programs with Graphs arXiv:1711.00740**Miltiadis Allamanis, Marc Brockschmidt, Mahmoud Khademi*2017 · 127 citations*](https://md.research.elamur.ai/free/en/papers/allamanis-2017-learning-represent-programs-graphs/)
151. [**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/)
152. [**Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training arXiv:2401.05566**Evan Hubinger, Carson Denison, Jesse Mu, et al.*2024 · 123 citations*](https://md.research.elamur.ai/free/en/papers/hubinger-2024-sleeper-agents-training-deceptive-llms/)
153. **[ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts](https://md.research.elamur.ai/free/en/papers/asai-2022-attempt-parameter-efficient-multi-task/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Akari Asai, Mohammadreza Salehi, Matthew Peters, et al.*2022 · 122 citations*
154. [**Making Neural QA as Simple as Possible but not Simpler arXiv:1703.04816**Dirk Weissenborn, Georg Wiese, Laura Seiffe*2017 · 122 citations*](https://md.research.elamur.ai/free/en/papers/weissenborn-2017-making-neural-qa-simple-possible/)
155. [**QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving MLSys 2025**Yujun Lin, Haotian Tang, Shang Yang, et al.*2024 · 120 citations*](https://md.research.elamur.ai/free/en/papers/lin-2024-qserve-w4a8kv4-quantization-system-co/)
156. [**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/)
157. [**Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering arXiv:2306.04136**Jinheon Baek, Alham Fikri Aji, Amir Saffari*2023 · 119 citations*](https://md.research.elamur.ai/free/en/papers/baek-2023-knowledge-augmented-language-model-prompting/)
158. **[ChID: A Large-scale Chinese IDiom Dataset for Cloze Test](https://md.research.elamur.ai/free/en/papers/zheng-2019-chid-large-scale-chinese/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chujie Zheng, Minlie Huang, Aixin Sun*2019 · 119 citations*
159. **[MetricX-24: The Google Submission to the WMT 2024 Metrics Shared Task](https://md.research.elamur.ai/free/en/papers/juraska-2024-metricx-24-google-submission-wmt/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Juraj Juraska, Daniel Deutsch, Mara Finkelstein, et al.*2024 · 118 citations*
160. **[MetricX-23: The Google Submission to the WMT 2023 Metrics Shared Task](https://md.research.elamur.ai/free/en/papers/juraska-2023-metricx-23-google-submission-wmt/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Juraj Juraska, Mara Finkelstein, Daniel Deutsch, et al.*2023 · 118 citations*
161. [**Inspecting and Editing Knowledge Representations in Language Models arXiv:2304.00740**Evan Hernandez, Belinda Z. Li, Jacob Andreas*2023 · 115 citations*](https://md.research.elamur.ai/free/en/papers/hernandez-2023-inspecting-editing-knowledge-representations-language/)
162. [**DuoAttention: Efficient Long-Context LLM Inference with Retrieval and Streaming Heads arXiv:2410.10819**Guangxuan Xiao, Jiaming Tang, Jingwei Zuo, et al.*2024 · 114 citations*](https://md.research.elamur.ai/free/en/papers/xiao-2024-duoattention-efficient-long-context-llm/)
163. **[Entity Linking in 100 Languages](https://md.research.elamur.ai/free/en/papers/botha-2020-entity-linking-100-languages/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jan A. Botha, Zifei Shan, Daniel Gillick*2020 · 114 citations*
164. **[WiCE: Real-World Entailment for Claims in Wikipedia](https://md.research.elamur.ai/free/en/papers/kamoi-2023-wice-real-world-entailment-claims/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ryo Kamoi, Tanya Goyal, Juan Diego Rodriguez, et al.*2023 · 113 citations*
165. **[Do Androids Laugh at Electric Sheep? Humor “Understanding” Benchmarks from The New Yorker Caption Contest](https://md.research.elamur.ai/free/en/papers/hessel-2023-do-androids-laugh-electric-sheep/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jack Hessel, Ana Marasovic, Jena D. Hwang, et al.*2023 · 112 citations*
166. [**Linear Transformers Are Secretly Fast Weight Programmers arXiv:2102.11174**Imanol Schlag, Kazuki Irie, Jurgen Schmidhuber*2021 · 111 citations*](https://md.research.elamur.ai/free/en/papers/schlag-2021-linear-transformers-secretly-fast-weight/)
167. [**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/)
168. **[ConSeC: Word Sense Disambiguation as Continuous Sense Comprehension](https://md.research.elamur.ai/free/en/papers/barba-2021-consec-word-sense-disambiguation-as/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Edoardo Barba, Luigi Procopio, Roberto Navigli*2021 · 109 citations*
169. [**Mechanistic Interpretability for AI Safety — A Review arXiv:2404.14082**Leonard Bereska, Efstratios Gavves*2024 · 108 citations*](https://md.research.elamur.ai/free/en/papers/bereska-2024-mechanistic-interpretability-ai-safety-review/)
170. [**HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation arXiv:2108.06712**Zhoujun Cheng, Haoyu Dong, Zhiruo Wang, et al.*2021 · 108 citations*](https://md.research.elamur.ai/free/en/papers/cheng-2021-hitab-hierarchical-table-dataset-question/)
171. **[An Empirical Study of Pre-trained Transformers for Arabic Information Extraction](https://md.research.elamur.ai/free/en/papers/lan-2020-empirical-study-pre-trained-transformers/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Wuwei Lan, Yang Chen, Wei Xu, et al.*2020 · 107 citations*
172. [**DAPPLE: A Pipelined Data Parallel Approach for Training Large Models arXiv:2007.01045**Shiqing Fan, Yi Rong, Chen Meng, et al.*2020 · 107 citations*](https://md.research.elamur.ai/free/en/papers/fan-2020-dapple-pipelined-data-parallel-approach/)
173. [**Reasoning Models Don't Always Say What They Think arXiv:2505.05410**Yanda Chen, Joe Benton, Ansh Radhakrishnan, et al.*2025 · 106 citations*](https://md.research.elamur.ai/free/en/papers/chen-2025-reasoning-models-don-t-always/)
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176. [**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/)
177. [**minicons: Enabling Flexible Behavioral and Representational Analyses of Transformer Language Models arXiv:2203.13112**Kanishka Misra*2022 · 102 citations*](https://md.research.elamur.ai/free/en/papers/misra-2022-minicons-enabling-flexible-behavioral-representational/)
178. [**Dataless Knowledge Fusion by Merging Weights of Language Models arXiv:2212.09849**Xisen Jin, Xiang Ren, Daniel Preotiuc-Pietro, et al.*2022 · 101 citations*](https://md.research.elamur.ai/free/en/papers/jin-2022-dataless-knowledge-fusion-merging-weights/)
179. [**FineWeb2: One Pipeline to Scale Them All — Adapting Pre-Training Data Processing to Every Language arXiv:2506.20920**Guilherme Penedo, Hynek Kydliček, Vinko Sabolčec, et al.*2025 · 99 citations*](https://md.research.elamur.ai/free/en/papers/penedo-2025-fineweb2-one-pipeline-scale-them/)
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184. [**From RAG to Memory: Non-Parametric Continual Learning for Large Language Models arXiv:2502.14802**Bernal Jimenez Gutierrez, Yiheng Shu, Weijian Qi, et al.*2025 · 90 citations*](https://md.research.elamur.ai/free/en/papers/gutierrez-2025-rag-memory-non-parametric-continual/)
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188. [**End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering arXiv:2106.05346**Devendra Singh Sachan, Siva Reddy, William Hamilton, et al.*2021 · 88 citations*](https://md.research.elamur.ai/free/en/papers/sachan-2021-end-end-training-multi-document/)
189. [**Foundational Challenges in Assuring Alignment and Safety of Large Language Models arXiv:2404.09932**Usman Anwar, Abulhair Saparov, Javier Rando, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/anwar-2024-foundational-challenges-assuring-alignment-safety/)
190. [**Liger Kernel: Efficient Triton Kernels for LLM Training arXiv:2410.10989**Pin-Lun Hsu, Yun Dai, Vignesh Kothapalli, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/hsu-2024-liger-kernel-efficient-triton-kernels/)
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195. [**SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval arXiv:2109.10086**Thibault Formal, Carlos Lassance, Benjamin Piwowarski, et al.*2021 · 83 citations*](https://md.research.elamur.ai/free/en/papers/formal-2021-splade-v2-sparse-lexical/)
196. [**Linearity of Relation Decoding in Transformer Language Models arXiv:2308.09124**Evan Hernandez, Arnab Sen Sharma, Tal Haklay, et al.*2023 · 82 citations*](https://md.research.elamur.ai/free/en/papers/hernandez-2023-linearity-relation-decoding-transformer-language/)
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