---
title: "Who cites Scaling Laws for Neural Language Models — page 2"
description: "1,823 papers of the Elamur Research library cite «Scaling Laws for Neural Language Models», found in their bibliographies · page 2 of 3…"
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# Who cites «Scaling Laws for Neural Language Models»

1,823 papers of our library cite it (of the 141,840 whose bibliographies we read) · 203 with a page here, the most cited first · page 2 of 3 · 101–200 · [← back to «Scaling Laws for Neural Language Models»](https://md.research.elamur.ai/free/en/papers/kaplan-2020-scaling-laws-neural-language-models/)

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101. [**TinyStories: How Small Can Language Models Be and Still Speak Coherent English?**Ronen Eldan, Yuanzhi Li*2023 · 202 citations*](https://md.research.elamur.ai/free/en/papers/eldan-2023-tinystories-small-language-models-still/)
102. [**COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining arXiv:2102.08473**Yu Meng, Chenyan Xiong, Payal Bajaj, et al.*2021 · 196 citations*](https://md.research.elamur.ai/free/en/papers/meng-2021-coco-lm-correcting-contrasting-text/)
103. [**RRHF: Rank Responses to Align Language Models with Human Feedback without tears arXiv:2304.05302**Zheng Yuan, Hongyi Yuan, Chuanqi Tan, et al.*2023 · 194 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2023-rrhf-rank-responses-align-language/)
104. **[Merging Models with Fisher-Weighted Averaging](https://md.research.elamur.ai/free/en/papers/matena-2021-merging-models-fisher-weighted-averaging/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Michael Matena, Colin Raffel*2021 · 191 citations*
105. [**LawBench: Benchmarking Legal Knowledge of Large Language Models arXiv:2309.16289**Zhiwei Fei, Xiaoyu Shen, Dawei Zhu, et al.*2023 · 186 citations*](https://md.research.elamur.ai/free/en/papers/fei-2023-lawbench-benchmarking-legal-knowledge-large/)
106. **[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 · 183 citations*
107. **[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 · 183 citations*
108. [**Pre-training via Paraphrasing arXiv:2006.15020**Mike Lewis, Marjan Ghazvininejad, Gargi Ghosh, et al.*2020 · 181 citations*](https://md.research.elamur.ai/free/en/papers/lewis-2020-pre-training-paraphrasing/)
109. **[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 · 179 citations*
110. [**Effective Long-Context Scaling of Foundation Models arXiv:2309.16039**Wenhan Xiong, Jingyu Liu, Igor Molybog, et al.*2023 · 177 citations*](https://md.research.elamur.ai/free/en/papers/xiong-2023-effective-long-context-scaling-foundation/)
111. [**8-bit Optimizers via Block-wise Quantization arXiv:2110.02861**Tim Dettmers, Mike Lewis, Sam Shleifer, et al.*2021 · 177 citations*](https://md.research.elamur.ai/free/en/papers/dettmers-2021-8-bit-optimizers-block-wise/)
112. [**ShortGPT: Layers in Large Language Models are More Redundant Than You Expect arXiv:2403.03853**Xin Men, Mingyu Xu, Qingyu Zhang, et al.*2024 · 167 citations*](https://md.research.elamur.ai/free/en/papers/men-2024-shortgpt-layers-large-language-models/)
113. **[Large Language Diffusion Models](https://md.research.elamur.ai/free/en/papers/nie-2025-large-language-diffusion-models/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Shen Nie, Fengqi Zhu, Zebin You, et al.*2025 · 164 citations*
114. [**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 · 163 citations*](https://md.research.elamur.ai/free/en/papers/zhuo-2024-bigcodebench-benchmarking-code-generation-diverse/)
115. [**Scaling Synthetic Data Creation with 1,000,000,000 Personas arXiv:2406.20094**Tao Ge, Xin Chan, Xiaoyang Wang, et al.*2024 · 160 citations*](https://md.research.elamur.ai/free/en/papers/ge-2024-scaling-synthetic-data-creation-1/)
116. [**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/)
117. **[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 · 156 citations*
118. [**HunyuanVideo: A Systematic Framework For Large Video Generative Models arXiv:2412.03603**Weijie Kong, Qi Tian, Zijian Zhang, et al.*2024 · 155 citations*](https://md.research.elamur.ai/free/en/papers/kong-2024-hunyuanvideo-systematic-framework-large/)
119. **[Language Model Behavior: A Comprehensive Survey](https://md.research.elamur.ai/free/en/papers/chang-2024-language-model-behavior-comprehensive-survey/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Tyler A. Chang, Benjamin K. Bergen*2024 · 148 citations*
120. [**The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset arXiv:2303.03915**Hugo Laurençon, Lucile Saulnier, Thomas Wang, et al.*2023 · 147 citations*](https://md.research.elamur.ai/free/en/papers/lauren-ccon-2023-bigscience-roots-corpus-1-6tb/)
121. **[How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition](https://md.research.elamur.ai/free/en/papers/dong-2024-how-abilities-large-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Guanting Dong, Hongyi Yuan, Keming Lu, et al.*2024 · 145 citations*
122. **[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*
123. [**PMC-LLaMA: Towards Building Open-source Language Models for Medicine arXiv:2304.14454**Chaoyi Wu, Weixiong Lin, Xiaoman Zhang, et al.*2023 · 144 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-pmc-llama-towards-building-open/)
124. [**MiniLLM: On-Policy Distillation of Large Language Models arXiv:2306.08543**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/)
125. [**Rethinking embedding coupling in pre-trained language models arXiv:2010.12821**Hyung Won Chung, Thibault Févry, Henry Tsai, et al.*2020 · 142 citations*](https://md.research.elamur.ai/free/en/papers/chung-2020-rethinking-embedding-coupling-pre-trained/)
126. [**DeepInception: Hypnotize Large Language Model to Be Jailbreaker arXiv:2311.03191**Xuan Li, Zhanke Zhou, Jianing Zhu, et al.*2023 · 138 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-deepinception-hypnotize-large-language-model/)
127. [**Efficient Training of Language Models to Fill in the Middle arXiv:2207.14255**Mohammad Bavarian, Heewoo Jun, Nikolas Tezak, et al.*2022 · 138 citations*](https://md.research.elamur.ai/free/en/papers/bavarian-2022-efficient-training-language-models-fill/)
128. **[Many-Shot In-Context Learning](https://md.research.elamur.ai/free/en/papers/agarwal-2024-many-shot-context-learning/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Rishabh Agarwal, Avi Singh, Lei M. Zhang, et al.*2024 · 134 citations*
129. **[Few-Shot Self-Rationalization with Natural Language Prompts](https://md.research.elamur.ai/free/en/papers/marasovic-2022-few-shot-self-rationalization-natural/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ana Marasovic, Iz Beltagy, Doug Downey, et al.*2022 · 134 citations*
130. [**Formal Mathematics Statement Curriculum Learning arXiv:2202.01344**Stanislas Polu, Jesse Michael Han, Kunhao Zheng, et al.*2022 · 132 citations*](https://md.research.elamur.ai/free/en/papers/polu-2022-formal-mathematics-statement-curriculum-learning/)
131. [**EuroLLM: Multilingual Language Models for Europe arXiv:2409.16235**Pedro Henrique Martins, Patrick Fernandes, Joao Alves, et al.*2024 · 131 citations*](https://md.research.elamur.ai/free/en/papers/martins-2024-eurollm-multilingual-language-models-europe/)
132. **[A Survey on Recent Advances in LLM-Based Multi-turn Dialogue Systems](https://md.research.elamur.ai/free/en/papers/yi-2024-survey-recent-advances-llm-based/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Zihao Yi, Jiarui Ouyang, Zhe Xu, et al.*2024 · 129 citations*
133. **[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 · 126 citations*
134. [**Large Language Model Alignment: A Survey arXiv:2309.15025**Tianhao Shen, Renren Jin, Yufei Huang, et al.*2023 · 123 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-large-language-model-alignment-survey/)
135. **[INSTRUCTSCORE: Towards Explainable Text Generation Evaluation with Automatic Feedback](https://md.research.elamur.ai/free/en/papers/xu-2023-instructscore-explainable-text-generation-evaluation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Wenda Xu, Danqing Wang, Liangming Pan, et al.*2023 · 122 citations*
136. **[Knowledge of cultural moral norms in large language models](https://md.research.elamur.ai/free/en/papers/ramezani-2023-knowledge-cultural-moral-norms-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Aida Ramezani, Yang Xu*2023 · 119 citations*
137. [**The case for 4-bit precision: k-bit Inference Scaling Laws arXiv:2212.09720**Tim Dettmers, Luke Zettlemoyer*2022 · 119 citations*](https://md.research.elamur.ai/free/en/papers/dettmers-2022-case-4-bit-precision-k/)
138. **[How Much Do Language Models Copy From Their Training Data? Evaluating Linguistic Novelty in Text Generation Using RAVEN](https://md.research.elamur.ai/free/en/papers/mccoy-2023-how-much-do-language-models/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**R. Thomas McCoy, Paul Smolensky, Tal Linzen, et al.*2023 · 116 citations*
139. **[The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning](https://md.research.elamur.ai/free/en/papers/kim-2023-cot-collection-improving-zero-shot/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Seungone Kim, Se Joo, Doyoung Kim, et al.*2023 · 116 citations*
140. [**Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models arXiv:2408.00724**Yangzhen Wu, Zhiqing Sun, Shanda Li, et al.*2024 · 114 citations*](https://md.research.elamur.ai/free/en/papers/wu-2024-inference-scaling-laws-empirical-analysis/)
141. [**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/)
142. [**Neural Networks and the Chomsky Hierarchy arXiv:2207.02098**Grégoire Delétang, Anian Ruoss, Jordi Grau-Moya, et al.*2022 · 110 citations*](https://md.research.elamur.ai/free/en/papers/deletang-2022-neural-networks-chomsky-hierarchy/)
143. [**Data Engineering for Scaling Language Models to 128K Context arXiv:2402.10171**Yao Fu, Rameswar Panda, Xinyao Niu, et al.*2024 · 107 citations*](https://md.research.elamur.ai/free/en/papers/fu-2024-data-engineering-scaling-language-models/)
144. [**Scaling Laws for Transfer arXiv:2102.01293**Danny Hernandez, Jared Kaplan, Tom Henighan, et al.*2021 · 102 citations*](https://md.research.elamur.ai/free/en/papers/hernandez-2021-scaling-laws-transfer/)
145. **[Eliciting the Translation Ability of Large Language Models via Multilingual Finetuning with Translation Instructions](https://md.research.elamur.ai/free/en/papers/li-2024-eliciting-translation-ability-large-language/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Jiahuan Li, Hao Zhou, Shujian Huang, et al.*2024 · 99 citations*
146. [**Inverse Scaling: When Bigger Isn't Better arXiv:2306.09479 · Transactions on Machine Learning Research (TMLR), 10/2023, https://openreview.net/forum?id=DwgRm72GQF**Ian R. McKenzie, Alexander Lyzhov, Michael Pieler, et al.*2023 · 99 citations*](https://md.research.elamur.ai/free/en/papers/mckenzie-2023-inverse-scaling-bigger-isn-t/)
147. **[Inseq: An Interpretability Toolkit for Sequence Generation Models](https://md.research.elamur.ai/free/en/papers/sarti-2023-inseq-interpretability-toolkit-sequence-generation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Gabriele Sarti, Nils Feldhus, Ludwig Sickert, et al.*2023 · 96 citations*
148. [**SemDeDup: Data-efficient learning at web-scale through semantic deduplication arXiv:2303.09540**Amro Abbas, Kushal Tirumala, Dániel Simig, et al.*2023 · 94 citations*](https://md.research.elamur.ai/free/en/papers/abbas-2023-semdedup-data-efficient-learning-web/)
149. **[Exploring the Potential of ChatGPT on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations](https://md.research.elamur.ai/free/en/papers/chan-2024-exploring-potential-chatgpt-sentence-level/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chunkit Chan, Cheng Jiayang, Weiqi Wang, et al.*2024 · 92 citations*
150. [**Skywork: A More Open Bilingual Foundation Model arXiv:2310.19341**Tianwen Wei, Liang Zhao, Lichang Zhang, et al.*2023 · 91 citations*](https://md.research.elamur.ai/free/en/papers/wei-2023-skywork-more-open-bilingual/)
151. [**Language Modeling Is Compression arXiv:2309.10668**Gregoire Deletang, Anian Ruoss, Paul-Ambroise Duquenne, et al.*2023 · 89 citations*](https://md.research.elamur.ai/free/en/papers/deletang-2023-language-modeling-compression/)
152. [**Small Language Models: Survey, Measurements, and Insights arXiv:2409.15790**Zhenyan Lu, Xiang Li, Dongqi Cai, et al.*2024 · 88 citations*](https://md.research.elamur.ai/free/en/papers/lu-2024-small-language-models-survey-measurements/)
153. [**A Survey of Large Language Models in Medicine: Progress, Application, and Challenge arXiv:2311.05112**Hongjian Zhou, Fenglin Liu, Boyang Gu, et al.*2023 · 87 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2023-survey-large-language-models-medicine/)
154. [**PromptNER: Prompting For Named Entity Recognition arXiv:2305.15444**Dhananjay Ashok, Zachary C. Lipton*2023 · 86 citations*](https://md.research.elamur.ai/free/en/papers/ashok-2023-promptner-prompting-named-entity-recognition/)
155. [**A Survey on Data Selection for Language Models arXiv:2402.16827**Alon Albalak, Yanai Elazar, Sang Michael Xie, et al.*2024 · 85 citations*](https://md.research.elamur.ai/free/en/papers/albalak-2024-survey-data-selection-language-models/)
156. **[When Is Multilinguality a Curse? Language Modeling for 250 High- and Low-Resource Languages](https://md.research.elamur.ai/free/en/papers/chang-2024-when-is-multilinguality-curse-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Tyler A. Chang, Catherine Arnett, Zhuowen Tu, et al.*2024 · 85 citations*
157. **[Multilingual Large Language Models Are Not (Yet) Code-Switchers](https://md.research.elamur.ai/free/en/papers/zhang-2023-multilingual-large-language-models-are/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ruochen Zhang, Samuel Cahyawijaya, Jan Christian Blaise Cruz, et al.*2023 · 85 citations*
158. **[CoLT5: Faster Long-Range Transformers with Conditional Computation](https://md.research.elamur.ai/free/en/papers/ainslie-2023-colt5-faster-long-range-transformers/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Joshua Ainslie, Tao Lei, Michiel de Jong, et al.*2023 · 83 citations*
159. **[D4: Improving LLM Pretraining via Document De-Duplication and Diversification](https://md.research.elamur.ai/free/en/papers/tirumala-2023-d4-improving-llm-pretraining-document/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Kushal Tirumala, Daniel Simig, Armen Aghajanyan, et al.*2023 · 79 citations*
160. **[Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling?](https://md.research.elamur.ai/free/en/papers/tay-2023-scaling-laws-vs-model-architectures/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yi Tay, Mostafa Dehghani, Samira Abnar, et al.*2023 · 79 citations*
161. **[Coreference Resolution through a seq2seq Transition-Based System](https://md.research.elamur.ai/free/en/papers/bohnet-2023-coreference-resolution-through-seq2seq-transition/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Bernd Bohnet, Chris Alberti, Michael Collins*2023 · 77 citations*
162. [**SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling arXiv:2312.15166**Dahyun Kim, Chanjun Park, Sanghoon Kim, et al.*2023 · 75 citations*](https://md.research.elamur.ai/free/en/papers/kim-2023-solar-10-7b-scaling-large/)
163. [**ReLoRA: High-Rank Training Through Low-Rank Updates arXiv:2307.05695**Vladislav Lialin, Namrata Shivagunde, Sherin Muckatira, et al.*2023 · 74 citations*](https://md.research.elamur.ai/free/en/papers/lialin-2023-relora-high-rank-training-through/)
164. **[Iterative Translation Refinement with Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2024-iterative-translation-refinement-large-language/) [Annual Conference of the European Association for Machine Translation (EAMT)](https://md.research.elamur.ai/free/en/venues/eamt/)**Pinzhen Chen, Zhicheng Guo, Barry Haddow, et al.*2024 · 73 citations*
165. [**OR-Bench: An Over-Refusal Benchmark for Large Language Models arXiv:2405.20947**Justin Cui, Wei-Lin Chiang, Ion Stoica, et al.*2024 · 73 citations*](https://md.research.elamur.ai/free/en/papers/cui-2024-bench-over-refusal-benchmark-large/)
166. **[KMMLU: Measuring Massive Multitask Language Understanding in Korean](https://md.research.elamur.ai/free/en/papers/son-2025-kmmlu-measuring-massive-multitask-language/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Guijin Son, Hanwool Lee, Sungdong Kim, et al.*2025 · 72 citations*
167. **[It's All in the Heads: Using Attention Heads as a Baseline for Cross-Lingual Transfer in Commonsense Reasoning](https://md.research.elamur.ai/free/en/papers/tikhonov-2021-it-s-all-heads-attention/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Alexey Tikhonov, Max Ryabinin*2021 · 69 citations*
168. [**Contrastive Decoding Improves Reasoning in Large Language Models arXiv:2309.09117**Sean O'Brien, Mike Lewis*2023 · 67 citations*](https://md.research.elamur.ai/free/en/papers/o-brien-2023-contrastive-decoding-improves-reasoning-large/)
169. [**Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning arXiv:2309.05444**Ted Zadouri, Ahmet Ustun, Arash Ahmadian, et al.*2023 · 66 citations*](https://md.research.elamur.ai/free/en/papers/zadouri-2023-pushing-mixture-experts-limit-extremely/)
170. [**Seed1.5-VL Technical Report arXiv:2505.07062**Dong Guo, Faming Wu, Feida Zhu, et al.*2025 · 64 citations*](https://md.research.elamur.ai/free/en/papers/guo-2025-seed1-5-vl-technical/)
171. **[SpiRit-LM: Interleaved Spoken and Written Language Model](https://md.research.elamur.ai/free/en/papers/nguyen-2025-spirit-lm-interleaved-spoken-written/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Tu Anh Nguyen, Benjamin Muller, Bokai Yu, et al.*2025 · 61 citations*
172. **[TARJAMAT: Evaluation of Bard and ChatGPT on Machine Translation of Ten Arabic Varieties](https://md.research.elamur.ai/free/en/papers/kadaoui-2023-tarjamat-evaluation-bard-chatgpt-machine/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Karima Kadaoui, Samar M. Magdy, Abdul Waheed, et al.*2023 · 60 citations*
173. [**Scaling Laws of RoPE-based Extrapolation arXiv:2310.05209**Xiaoran Liu, Hang Yan, Shuo Zhang, et al.*2023 · 53 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-scaling-laws-rope-based/)
174. [**Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws arXiv:2401.00448**Nikhil Sardana, Jacob Portes, Sasha Doubov, et al.*2024 · 51 citations*](https://md.research.elamur.ai/free/en/papers/sardana-2024-chinchilla-optimal-accounting-inference-language/)
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