---
title: "Who cites Cyprien de Masson — page 3 — 1,364 citing papers"
description: "1,364 papers of the Elamur Research library cite the works of Cyprien de Masson d'Autume, found in their bibliographies · page 3 of 5…"
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---
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# Who cites Cyprien de Masson d'Autume

1,404 citations of their works from 1,364 papers of our library (of the 149,439 whose bibliographies we read) · 438 with a page here, the most cited first · page 3 of 5 · 201–300 · [← Cyprien de Masson d'Autume](https://md.research.elamur.ai/free/en/authors/cyprien-de-masson-d-autume/)

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201. **[CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs](https://md.research.elamur.ai/free/en/papers/wang-2024-charxiv-charting-gaps-realistic-chart/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zirui Wang, Mengzhou Xia, Luxi He, et al.*2024 · 119 citations*
202. **[MapCoder: Multi-Agent Code Generation for Competitive Problem Solving](https://md.research.elamur.ai/free/en/papers/islam-2024-mapcoder-multi-agent-code/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Md. Ashraful Islam, Mohammed Eunus Ali, Md Rizwan Parvez*2024 · 118 citations*
203. [**VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs arXiv:2406.07476**Zesen Cheng, Sicong Leng, Hang Zhang, et al.*2024 · 118 citations*](https://md.research.elamur.ai/free/en/papers/cheng-2024-videollama-2-advancing-spatial-temporal/)
204. **[Natural Language to Code Translation with Execution](https://md.research.elamur.ai/free/en/papers/shi-2022-natural-language-code-translation-execution/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Freda Shi, Daniel Fried, Marjan Ghazvininejad, et al.*2022 · 116 citations*
205. **[LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Models](https://md.research.elamur.ai/free/en/papers/zhang-2025-lmms-eval-reality-check-evaluation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kaichen Zhang, Bo Li, Peiyuan Zhang, et al.*2025 · 115 citations*
206. [**Fundamental Limitations of Alignment in Large Language Models arXiv:2304.11082**Yotam Wolf, Noam Wies, Oshri Avnery, et al.*2023 · 114 citations*](https://md.research.elamur.ai/free/en/papers/wolf-2023-fundamental-limitations-alignment-large-language/)
207. [**Branch-Train-Merge: Embarrassingly Parallel Training of Expert Language Models arXiv:2208.03306**Margaret Li, Suchin Gururangan, Tim Dettmers, et al.*2022 · 114 citations*](https://md.research.elamur.ai/free/en/papers/li-2022-branch-train-merge-embarrassingly-parallel/)
208. **[Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations](https://md.research.elamur.ai/free/en/papers/yoo-2022-ground-truth-labels-matter-deeper/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Kang Min Yoo, Junyeob Kim, Hyuhng Joon Kim, et al.*2022 · 114 citations*
209. [**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/)
210. [**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/)
211. [**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/)
212. [**Can Machines Learn Morality? The Delphi Experiment arXiv:2110.07574**Liwei Jiang, Jena D. Hwang, Chandra Bhagavatula, et al.*2021 · 110 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2021-machines-learn-morality-delphi-experiment/)
213. [**Better Zero-Shot Reasoning with Role-Play Prompting arXiv:2308.07702**Aobo Kong, Shiwan Zhao, Hao Chen, et al.*2023 · 109 citations*](https://md.research.elamur.ai/free/en/papers/kong-2023-better-zero-shot-reasoning-role/)
214. **[CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors](https://md.research.elamur.ai/free/en/papers/li-2023-codeie-large-code-generation-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Peng Li, Tianxiang Sun, Qiong Tang, et al.*2023 · 109 citations*
215. [**SemDeDup: Data-efficient learning at web-scale through semantic deduplication arXiv:2303.09540**Amro Abbas, Kushal Tirumala, Dániel Simig, et al.*2023 · 108 citations*](https://md.research.elamur.ai/free/en/papers/abbas-2023-semdedup-data-efficient-learning-web/)
216. **[Multi-Step Reasoning with Large Language Models, a Survey](https://md.research.elamur.ai/free/en/papers/plaat-2024-multi-step-reasoning-large-language/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Aske Plaat, Annie Wong, Suzan Verberne, et al.*2024 · 106 citations*
217. **[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*
218. [**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 · 105 citations*](https://md.research.elamur.ai/free/en/papers/mckenzie-2023-inverse-scaling-bigger-isn-t/)
219. **[Self-Edit: Fault-Aware Code Editor for Code Generation](https://md.research.elamur.ai/free/en/papers/zhang-2023-self-edit-fault-aware-code/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kechi Zhang, Zhuo Li, Jia Li, et al.*2023 · 105 citations*
220. **[Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting Evidence](https://md.research.elamur.ai/free/en/papers/chen-2022-rich-knowledge-sources-bring-complex/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Hung-Ting Chen, Michael Zhang, Eunsol Choi*2022 · 105 citations*
221. [**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/)
222. [**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/)
223. **[LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language Models](https://md.research.elamur.ai/free/en/papers/park-2022-lut-gemm-quantized-matrix-multiplication/) [International Conference on Learning Representations (ICLR)](https://md.research.elamur.ai/free/en/venues/international-conference-on-learning-representations-iclr/)**Gunho Park, Baeseong Park, Minsub Kim, et al.*2022 · 103 citations*
224. [**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/)
225. [**Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints arXiv:2212.05055**Aran Komatsuzaki, Joan Puigcerver, James Lee-Thorp, et al.*2022 · 98 citations*](https://md.research.elamur.ai/free/en/papers/komatsuzaki-2022-sparse-upcycling-training-mixture-experts/)
226. [**TextGrad: Automatic "Differentiation" via Text arXiv:2406.07496**Mert Yuksekgonul, Federico Bianchi, Joseph Boen, et al.*2024 · 96 citations*](https://md.research.elamur.ai/free/en/papers/yuksekgonul-2024-textgrad-automatic-differentiation-text/)
227. [**Identifying the Risks of LM Agents with an LM-Emulated Sandbox arXiv:2309.15817**Yangjun Ruan, Honghua Dong, Andrew Wang, et al.*2023 · 96 citations*](https://md.research.elamur.ai/free/en/papers/ruan-2023-identifying-risks-lm-agents-lm/)
228. [**A Survey on Data Selection for Language Models arXiv:2402.16827**Alon Albalak, Yanai Elazar, Sang Michael Xie, et al.*2024 · 95 citations*](https://md.research.elamur.ai/free/en/papers/albalak-2024-survey-data-selection-language-models/)
229. **[InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedback](https://md.research.elamur.ai/free/en/papers/yang-2023-intercode-standardizing-benchmarking-interactive-coding/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**John Yang, Akshara Prabhakar, Karthik Narasimhan, et al.*2023 · 95 citations*
230. **[NLPositionality: Characterizing Design Biases of Datasets and Models](https://md.research.elamur.ai/free/en/papers/santy-2023-nlpositionality-characterizing-design-biases-datasets/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Sebastin Santy, Jenny Liang, Ronan Le Bras, et al.*2023 · 95 citations*
231. [**Advancing LLM Reasoning Generalists with Preference Trees arXiv:2404.02078**Lifan Yuan, Ganqu Cui, Hanbin Wang, et al.*2024 · 94 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2024-advancing-llm-reasoning-generalists-preference/)
232. **[Boosting Language Models Reasoning with Chain-of-Knowledge Prompting](https://md.research.elamur.ai/free/en/papers/wang-2024-boosting-language-models-reasoning-chain/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jianing Wang, Qiushi Sun, Xiang Li, et al.*2024 · 94 citations*
233. [**CodeGen2: Lessons for Training LLMs on Programming and Natural Languages arXiv:2305.02309**Erik Nijkamp, Hiroaki Hayashi, Caiming Xiong, et al.*2023 · 94 citations*](https://md.research.elamur.ai/free/en/papers/nijkamp-2023-codegen2-lessons-training-llms-programming/)
234. [**Training Socially Aligned Language Models on Simulated Social Interactions arXiv:2305.16960**Ruibo Liu, Ruixin Yang, Chenyan Jia, et al.*2023 · 94 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-training-socially-aligned-language-models/)
235. **[LLMs Are Few-Shot In-Context Low-Resource Language Learners](https://md.research.elamur.ai/free/en/papers/cahyawijaya-2024-llms-are-few-shot-context/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Samuel Cahyawijaya, Holy Lovenia, Pascale Fung*2024 · 93 citations*
236. [**Universal Self-Consistency for Large Language Model Generation arXiv:2311.17311**Xinyun Chen, Renat Aksitov, Uri Alon, et al.*2023 · 92 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-universal-self-consistency-large-language/)
237. [**Language Modeling Is Compression arXiv:2309.10668**Gregoire Deletang, Anian Ruoss, Paul-Ambroise Duquenne, et al.*2023 · 91 citations*](https://md.research.elamur.ai/free/en/papers/deletang-2023-language-modeling-compression/)
238. [**Better & Faster Large Language Models via Multi-token Prediction arXiv:2404.19737**Fabian Gloeckle, Badr Youbi Idrissi, Baptiste Roziere, et al.*2024 · 90 citations*](https://md.research.elamur.ai/free/en/papers/gloeckle-2024-better-faster-large-language-models/)
239. **[Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling](https://md.research.elamur.ai/free/en/papers/maini-2024-rephrasing-web-recipe-compute-data/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Pratyush Maini, Skyler Seto, Richard Bai, et al.*2024 · 90 citations*
240. [**AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation arXiv:2312.13010**Dong Huang, Jie M.Zhang, Michael Luck, et al.*2023 · 90 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-agentcoder-multi-agent-based/)
241. **[Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey](https://md.research.elamur.ai/free/en/papers/kumar-2023-language-generation-models-can-cause/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Sachin Kumar, Vidhisha Balachandran, Lucille Njoo, et al.*2023 · 90 citations*
242. [**Planning with Large Language Models for Code Generation arXiv:2303.05510**Shun Zhang, Zhenfang Chen, Yikang Shen, et al.*2023 · 90 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-planning-large-language-models-code/)
243. [**Automatically Auditing Large Language Models via Discrete Optimization arXiv:2303.04381**Erik Jones, Anca Dragan, Aditi Raghunathan, et al.*2023 · 89 citations*](https://md.research.elamur.ai/free/en/papers/jones-2023-automatically-auditing-large-language-models/)
244. **[LAMBADA: Backward Chaining for Automated Reasoning in Natural Language](https://md.research.elamur.ai/free/en/papers/kazemi-2023-lambada-backward-chaining-automated-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mehran Kazemi, Najoung Kim, Deepti Bhatia, et al.*2023 · 89 citations*
245. [**MultiPL-E: A Scalable and Extensible Approach to Benchmarking Neural Code Generation arXiv:2208.08227**Federico Cassano, John Gouwar, Daniel Nguyen, et al.*2022 · 88 citations*](https://md.research.elamur.ai/free/en/papers/cassano-2022-multipl-e-scalable-extensible-approach/)
246. **[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 · 86 citations*
247. [**Multi-lingual Evaluation of Code Generation Models arXiv:2210.14868**Ben Athiwaratkun, Sanjay Krishna Gouda, Zijian Wang, et al.*2022 · 86 citations*](https://md.research.elamur.ai/free/en/papers/athiwaratkun-2022-multi-lingual-evaluation-code-generation/)
248. [**AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model arXiv:2208.01448**Saleh Soltan, Shankar Ananthakrishnan, Jack FitzGerald, et al.*2022 · 84 citations*](https://md.research.elamur.ai/free/en/papers/soltan-2022-alexatm-20b-few-shot-learning/)
249. [**Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks arXiv:2310.10844**Erfan Shayegani, Md Abdullah Al Mamun, Yu Fu, et al.*2023 · 83 citations*](https://md.research.elamur.ai/free/en/papers/shayegani-2023-survey-vulnerabilities-large-language-models/)
250. [**Is Self-Repair a Silver Bullet for Code Generation? arXiv:2306.09896**Theo X. Olausson, Jeevana Priya Inala, Chenglong Wang, et al.*2024 · 82 citations*](https://md.research.elamur.ai/free/en/papers/olausson-2024-self-repair-silver-bullet-code/)
251. [**A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well? arXiv:2503.24235**Qiyuan Zhang, Fuyuan Lyu, Zexu Sun, et al.*2025 · 79 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2025-survey-test-time-scaling-large/)
252. [**LEGO-Prover: Neural Theorem Proving with Growing Libraries arXiv:2310.00656**Haiming Wang, Huajian Xin, Chuanyang Zheng, et al.*2023 · 79 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-lego-prover-neural-theorem-proving/)
253. **[AudioBench: A Universal Benchmark for Audio Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2025-audiobench-universal-benchmark-audio-large/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Bin Wang, Xunlong Zou, Geyu Lin, et al.*2025 · 77 citations*
254. [**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 · 77 citations*](https://md.research.elamur.ai/free/en/papers/kim-2023-solar-10-7b-scaling-large/)
255. [**Llama-Nemotron: Efficient Reasoning Models arXiv:2505.00949**Akhiad Bercovich, Itay Levy, Izik Golan, et al.*2025 · 76 citations*](https://md.research.elamur.ai/free/en/papers/bercovich-2025-llama-nemotron-efficient-reasoning-models/)
256. **[MANTIS: Interleaved Multi-Image Instruction Tuning](https://md.research.elamur.ai/free/en/papers/jiang-2024-mantis-interleaved-multi-image-instruction/) [Transactions on Machine Learning Research](https://md.research.elamur.ai/free/en/venues/transactions-on-machine-learning-research/)**Dongfu Jiang, Xuan He, Huaye Zeng, et al.*2024 · 74 citations*
257. [**QuRating: Selecting High-Quality Data for Training Language Models arXiv:2402.09739**Alexander Wettig, Aatmik Gupta, Saumya Malik, et al.*2024 · 74 citations*](https://md.research.elamur.ai/free/en/papers/wettig-2024-qurating-selecting-high-quality-data/)
258. [**Large Language Models as Analogical Reasoners arXiv:2310.01714**Michihiro Yasunaga, Xinyun Chen, Yujia Li, et al.*2023 · 74 citations*](https://md.research.elamur.ai/free/en/papers/yasunaga-2023-large-language-models-analogical-reasoners/)
259. [**Long Context Transfer from Language to Vision arXiv:2406.16852**Peiyuan Zhang, Kaichen Zhang, Bo Li, et al.*2024 · 73 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-long-context-transfer-language-vision/)
260. [**TACO: Topics in Algorithmic COde generation dataset arXiv:2312.14852**Rongao Li, Jie Fu, Bo-Wen Zhang, et al.*2023 · 73 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-taco-topics-algorithmic-code-generation/)
261. [**What Language Model to Train if You Have One Million GPU Hours? arXiv:2210.15424**Teven Le Scao, Thomas Wang, Daniel Hesslow, et al.*2022 · 73 citations*](https://md.research.elamur.ai/free/en/papers/scao-2022-language-model-train-if-have/)
262. **[Few-Shot Semantic Parsing with Language Models Trained on Code](https://md.research.elamur.ai/free/en/papers/shin-2022-few-shot-semantic-parsing-language/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Richard Shin, Benjamin Van Durme*2022 · 72 citations*
263. [**InPars: Data Augmentation for Information Retrieval using Large Language Models arXiv:2202.05144**Luiz Bonifacio, Hugo Abonizio, Marzieh Fadaee, et al.*2022 · 72 citations*](https://md.research.elamur.ai/free/en/papers/bonifacio-2022-inpars-data-augmentation-information-retrieval/)
264. [**The Ghost in the Machine has an American accent: value conflict in GPT-3 arXiv:2203.07785**Rebecca L Johnson, Giada Pistilli, Natalia Menedez-Gonzalez, et al.*2022 · 72 citations*](https://md.research.elamur.ai/free/en/papers/johnson-2022-ghost-machine-has-american-accent/)
265. **[Transformer Grammars: Augmenting Transformer Language Models with Syntactic Inductive Biases at Scale](https://md.research.elamur.ai/free/en/papers/sartran-2022-transformer-grammars-augmenting-transformer-language/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Laurent Sartran, Samuel Barrett, Adhiguna Kuncoro, et al.*2022 · 72 citations*
266. **[Entailer: Answering Questions with Faithful and Truthful Chains of Reasoning](https://md.research.elamur.ai/free/en/papers/tafjord-2022-entailer-answering-questions-faithful/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Oyvind Tafjord, Bhavana Dalvi Mishra, Peter Clark*2022 · 71 citations*
267. **[English Intermediate-Task Training Improves Zero-Shot Cross-Lingual Transfer Too](https://md.research.elamur.ai/free/en/papers/phang-2020-english-intermediate-task-training-improves/) [Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL)](https://md.research.elamur.ai/free/en/venues/aacl/)**Jason Phang, Iacer Calixto, Phu Mon Htut, et al.*2020 · 70 citations*
268. **[Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation](https://md.research.elamur.ai/free/en/papers/zhang-2024-self-alignment-factuality-mitigating-hallucinations/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaoying Zhang, Baolin Peng, Ye Tian, et al.*2024 · 69 citations*
269. **[Execution-Based Evaluation for Open-Domain Code Generation](https://md.research.elamur.ai/free/en/papers/wang-2023-execution-based-evaluation-open-domain/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhiruo Wang, Shuyan Zhou, Daniel Fried, et al.*2023 · 69 citations*
270. [**Competitive Programming with Large Reasoning Models arXiv:2502.06807**OpenAI: Ahmed El-Kishky, Alexander Wei, Andre Saraiva, et al.*2025 · 68 citations*](https://md.research.elamur.ai/free/en/papers/el-kishky-2025-competitive-programming-large-reasoning-models/)
271. **[Nemotron-CC: Transforming Common Crawl into a Refined Long-Horizon Pretraining Dataset](https://md.research.elamur.ai/free/en/papers/su-2025-nemotron-cc-transforming-common-crawl/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dan Su, Kezhi Kong, Ying Lin, et al.*2025 · 65 citations*
272. **[Questions Are All You Need to Train a Dense Passage Retriever](https://md.research.elamur.ai/free/en/papers/sachan-2023-questions-are-all-you-need/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Devendra Singh Sachan, Mike Lewis, Dani Yogatama, et al.*2023 · 65 citations*
273. [**A Comprehensive Survey on Long Context Language Modeling arXiv:2503.17407**Jiaheng Liu, Dawei Zhu, Zhiqi Bai, et al.*2025 · 64 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-comprehensive-survey-long-context-language/)
274. [**Layer by Layer: Uncovering Hidden Representations in Language Models arXiv:2502.02013**Oscar Skean, Md Rifat Arefin, Dan Zhao, et al.*2025 · 64 citations*](https://md.research.elamur.ai/free/en/papers/skean-2025-layer-layer-uncovering-hidden-representations/)
275. **[SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution](https://md.research.elamur.ai/free/en/papers/wei-2025-swe-rl-advancing-llm-reasoning/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yuxiang Wei, Olivier Duchenne, Jade Copet, et al.*2025 · 64 citations*
276. [**Evaluation of OpenAI o1: Opportunities and Challenges of AGI arXiv:2409.18486**Tianyang Zhong, Zhengliang Liu, Yi Pan, et al.*2024 · 64 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2024-evaluation-openai-o1-opportunities-challenges/)
277. [**OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models arXiv:2402.01739**Fuzhao Xue, Zian Zheng, Yao Fu, et al.*2024 · 64 citations*](https://md.research.elamur.ai/free/en/papers/xue-2024-openmoe-early-effort-open-mixture/)
278. **[Can Language Models Handle Recursively Nested Grammatical Structures? A Case Study on Comparing Models and Humans](https://md.research.elamur.ai/free/en/papers/lampinen-2024-can-language-models-handle-recursively/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Andrew Lampinen*2024 · 63 citations*
279. [**ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and Generation arXiv:2112.12731**Shuohuan Wang, Yu Sun, Yang Xiang, et al.*2021 · 63 citations*](https://md.research.elamur.ai/free/en/papers/wang-2021-ernie-3-0-titan/)
280. **[SatLM: Satisfiability-Aided Language Models Using Declarative Prompting](https://md.research.elamur.ai/free/en/papers/ye-2023-satlm-satisfiability-aided-language-models/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Xi Ye, Qiaochu Chen, Isil Dillig, et al.*2023 · 62 citations*
281. **[NaturalProver: Grounded Mathematical Proof Generation with Language Models](https://md.research.elamur.ai/free/en/papers/welleck-2022-naturalprover-grounded-mathematical-proof-generation/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Sean Welleck, Jiacheng Liu, Ximing Lu, et al.*2022 · 62 citations*
282. **[Instruction Pre-Training: Language Models are Supervised Multitask Learners](https://md.research.elamur.ai/free/en/papers/cheng-2024-instruction-pre-training-language-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Daixuan Cheng, Yuxian Gu, Shaohan Huang, et al.*2024 · 59 citations*
283. **[ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models](https://md.research.elamur.ai/free/en/papers/liu-2025-prorl-prolonged-reinforcement-learning-expands/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Mingjie Liu, Shizhe Diao, Ximing Lu, et al.*2025 · 58 citations*
284. [**Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws arXiv:2401.00448**Nikhil Sardana, Jacob Portes, Sasha Doubov, et al.*2024 · 57 citations*](https://md.research.elamur.ai/free/en/papers/sardana-2024-chinchilla-optimal-accounting-inference-language/)
285. [**Dialect prejudice predicts AI decisions about people's character, employability, and criminality arXiv:2403.00742**Valentin Hofmann, Pratyusha Ria Kalluri, Dan Jurafsky, et al.*2024 · 57 citations*](https://md.research.elamur.ai/free/en/papers/hofmann-2024-dialect-prejudice-predicts-ai-decisions/)
286. [**RegMix: Data Mixture as Regression for Language Model Pre-training arXiv:2407.01492**Qian Liu, Xiaosen Zheng, Niklas Muennighoff, et al.*2024 · 57 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-regmix-data-mixture-regression-language/)
287. **[OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI](https://md.research.elamur.ai/free/en/papers/huang-2024-olympicarena-benchmarking-multi-discipline-cognitive/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhen Huang, Zengzhi Wang, Shijie Xia, et al.*2024 · 56 citations*
288. **[Parsel: Algorithmic Reasoning with Language Models by Composing Decompositions](https://md.research.elamur.ai/free/en/papers/zelikman-2023-parsel-algorithmic-reasoning-language-models/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Eric Zelikman, Qian Huang, Gabriel Poesia, et al.*2023 · 56 citations*
289. [**AI Agents That Matter arXiv:2407.01502**Sayash Kapoor, Benedikt Stroebl, Zachary S. Siegel, et al.*2024 · 55 citations*](https://md.research.elamur.ai/free/en/papers/kapoor-2024-ai-agents-that-matter/)
290. [**AutoML-GPT: Automatic Machine Learning with GPT arXiv:2305.02499**Shujian Zhang, Chengyue Gong, Lemeng Wu, et al.*2023 · 52 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-automl-gpt-automatic-machine-learning/)
291. [**From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models arXiv:2406.16838**Sean Welleck, Amanda Bertsch, Matthew Finlayson, et al.*2024 · 51 citations*](https://md.research.elamur.ai/free/en/papers/welleck-2024-decoding-meta-generation-inference-time/)
292. [**RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning arXiv:2410.02089**Jonas Gehring, Kunhao Zheng, Jade Copet, et al.*2024 · 51 citations*](https://md.research.elamur.ai/free/en/papers/gehring-2024-rlef-grounding-code-llms-execution/)
293. [**On the application of Large Language Models for language teaching and assessment technology arXiv:2307.08393**Andrew Caines, Luca Benedetto, Shiva Taslimipoor, et al.*2023 · 51 citations*](https://md.research.elamur.ai/free/en/papers/caines-2023-application-large-language-models-language/)
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295. [**PanGu-Coder: Program Synthesis with Function-Level Language Modeling arXiv:2207.11280**Fenia Christopoulou, Gerasimos Lampouras, Milan Gritta, et al.*2022 · 49 citations*](https://md.research.elamur.ai/free/en/papers/christopoulou-2022-pangu-coder-program-synthesis/)
296. **[IndicLLMSuite: A Blueprint for Creating Pre-training and Fine-Tuning Datasets for Indian Languages](https://md.research.elamur.ai/free/en/papers/khan-2024-indicllmsuite-blueprint-creating-pre-training/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mohammed Safi Ur Rahman Khan, Priyam Mehta, Ananth Sankar, et al.*2024 · 48 citations*
297. **[MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures](https://md.research.elamur.ai/free/en/papers/ni-2024-mixeval-deriving-wisdom-crowd-llm/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jinjie Ni, Fuzhao Xue, Xiang Yue, et al.*2024 · 47 citations*
298. [**Language models scale reliably with over-training and on downstream tasks arXiv:2403.08540**Samir Yitzhak Gadre, Georgios Smyrnis, Vaishaal Shankar, et al.*2024 · 46 citations*](https://md.research.elamur.ai/free/en/papers/gadre-2024-language-models-scale-reliably-over/)
299. [**Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies arXiv:2407.13623**Chaofan Tao, Qian Liu, Longxu Dou, et al.*2024 · 46 citations*](https://md.research.elamur.ai/free/en/papers/tao-2024-scaling-laws-vocabulary-larger-models/)
300. **[AlGhafa Evaluation Benchmark for Arabic Language Models](https://md.research.elamur.ai/free/en/papers/almazrouei-2023-alghafa-evaluation-benchmark-arabic-language/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Ebtesam Almazrouei, Ruxandra Cojocaru, Michele Baldo, et al.*2023 · 46 citations*

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