---
title: "Who cites Li Lucy — 451 papers of our library — Elamur Research"
description: "451 papers of the Elamur Research library cite the works of Li Lucy, found in their bibliographies · page 1 of 2 · 1–100; the most cited first."
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# Who cites Li Lucy

457 citations of their works from 451 papers of our library (of the 149,439 whose bibliographies we read) · 125 with a page here, the most cited first · page 1 of 2 · 1–100 · [← Li Lucy](https://md.research.elamur.ai/free/en/authors/li-lucy/)

**1** [2](https://md.research.elamur.ai/free/en/authors/li-lucy/cited-by/page/2/) [next →](https://md.research.elamur.ai/free/en/authors/li-lucy/cited-by/page/2/)

1. **[A Survey of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/) [Frontiers of Computer Science](https://md.research.elamur.ai/free/en/venues/frontiers-of-computer-science/)**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,875 citations*
2. **[Can Large Language Models Transform Computational Social Science?](https://md.research.elamur.ai/free/en/papers/ziems-2023-large-language-models-transform-computational/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Caleb Ziems, William Held, Omar Shaikh, et al.*2023 · 790 citations*
3. [**Ethical and social risks of harm from Language Models arXiv:2112.04359**Laura Weidinger, John Mellor, Maribeth Rauh, et al.*2021 · 456 citations*](https://md.research.elamur.ai/free/en/papers/weidinger-2021-ethical-social-risks-harm-language/)
4. **[Experience Grounds Language](https://md.research.elamur.ai/free/en/papers/bisk-2020-experience-grounds-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yonatan Bisk, Ari Holtzman, Jesse Thomason, et al.*2020 · 439 citations*
5. **[The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale](https://md.research.elamur.ai/free/en/papers/penedo-2024-fineweb-datasets-decanting-web-finest/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Guilherme Penedo, Hynek Kydlíček, Loubna Ben allal, et al.*2024 · 434 citations*
6. **[xLSTM: Extended Long Short-Term Memory](https://md.research.elamur.ai/free/en/papers/beck-2024-xlstm-extended-long-short-term/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Maximilian Beck, Korbinian Pöppel, Markus Spanring, et al.*2024 · 296 citations*
7. [**Towards Measuring the Representation of Subjective Global Opinions in Language Models arXiv:2306.16388**Esin Durmus, Karina Nguyen, Thomas I. Liao, et al.*2023 · 270 citations*](https://md.research.elamur.ai/free/en/papers/durmus-2023-towards-measuring-representation-subjective-global/)
8. [**MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies arXiv:2404.06395**Shengding Hu, Yuge Tu, Xu Han, et al.*2024 · 269 citations*](https://md.research.elamur.ai/free/en/papers/hu-2024-minicpm-unveiling-potential-small-language/)
9. **[RedPajama: an Open Dataset for Training Large Language Models](https://md.research.elamur.ai/free/en/papers/weber-2024-redpajama-open-dataset-training-large/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Maurice Weber, Daniel Fu, Quentin Anthony, et al.*2024 · 260 citations*
10. [**SocialIQA: Commonsense Reasoning about Social Interactions arXiv:1904.09728**Maarten Sap, Hannah Rashkin, Derek Chen, et al.*2019 · 259 citations*](https://md.research.elamur.ai/free/en/papers/sap-2019-socialiqa-commonsense-reasoning-about-social/)
11. **[Societal Biases in Language Generation: Progress and Challenges](https://md.research.elamur.ai/free/en/papers/sheng-2021-societal-biases-language-generation-progress/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Emily Sheng, Kai-Wei Chang, Prem Natarajan, et al.*2021 · 257 citations*
12. [**Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model arXiv:2402.07827**Ahmet Üstün, Viraat Aryabumi, Zheng-Xin Yong, et al.*2024 · 253 citations*](https://md.research.elamur.ai/free/en/papers/ustun-2024-aya-model-instruction-finetuned-open/)
13. [**Red teaming ChatGPT via Jailbreaking: Bias, Robustness, Reliability and Toxicity arXiv:2301.12867**Terry Yue Zhuo, Yujin Huang, Chunyang Chen, et al.*2023 · 249 citations*](https://md.research.elamur.ai/free/en/papers/zhuo-2023-red-teaming-chatgpt-jailbreaking-bias/)
14. **[Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models](https://md.research.elamur.ai/free/en/papers/cheng-2023-marked-personas-natural-language-prompts/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Myra Cheng, Esin Durmus, Dan Jurafsky*2023 · 246 citations*
15. [**WildChat: 1M ChatGPT Interaction Logs in the Wild arXiv:2405.01470**Wenting Zhao, Xiang Ren, Jack Hessel, et al.*2024 · 239 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2024-wildchat-1m-chatgpt-interaction-logs/)
16. [**ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning arXiv:2304.06588**Petter Tornberg*2023 · 231 citations*](https://md.research.elamur.ai/free/en/papers/tornberg-2023-chatgpt-4-outperforms-experts-crowd/)
17. [**Prompting GPT-3 To Be Reliable arXiv:2210.09150**Chenglei Si, Zhe Gan, Zhengyuan Yang, et al.*2022 · 210 citations*](https://md.research.elamur.ai/free/en/papers/si-2022-prompting-gpt-3-reliable/)
18. **[Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning](https://md.research.elamur.ai/free/en/papers/singh-2024-aya-dataset-open-access-collection/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shivalika Singh, Freddie Vargus, Daniel D'souza, et al.*2024 · 207 citations*
19. **[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*
20. **[Investigating Cultural Alignment of Large Language Models](https://md.research.elamur.ai/free/en/papers/alkhamissi-2024-investigating-cultural-alignment-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Badr AlKhamissi, Muhammad ElNokrashy, Mai Alkhamissi, et al.*2024 · 183 citations*
21. [**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/)
22. [**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/)
23. **[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*
24. **[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*
25. **[SCOTT: Self-Consistent Chain-of-Thought Distillation](https://md.research.elamur.ai/free/en/papers/wang-2023-scott-self-consistent-chain-thought/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Peifeng Wang, Zhengyang Wang, Zheng Li, et al.*2023 · 146 citations*
26. [**Secrets of RLHF in Large Language Models Part I: PPO arXiv:2307.04964**Rui Zheng, Shihan Dou, Songyang Gao, et al.*2023 · 135 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2023-secrets-rlhf-large-language-models/)
27. [**Olmo 3 arXiv:2512.13961**Team Olmo: Allyson Ettinger, Amanda Bertsch, Bailey Kuehl, et al.*2025 · 130 citations*](https://md.research.elamur.ai/free/en/papers/ettinger-2025-olmo-3/)
28. [**Fanar: An Arabic-Centric Multimodal Generative AI Platform arXiv:2501.13944**Fanar Team: Ummar Abbas, Mohammad Shahmeer Ahmad, Firoj Alam, et al.*2025 · 127 citations*](https://md.research.elamur.ai/free/en/papers/abbas-2025-fanar-arabic-centric-multimodal-generative/)
29. [**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/)
30. [**OpenThoughts: Data Recipes for Reasoning Models arXiv:2506.04178**Etash Guha, Ryan Marten, Sedrick Keh, et al.*2025 · 122 citations*](https://md.research.elamur.ai/free/en/papers/guha-2025-openthoughts-data-recipes-reasoning-models/)
31. **[Can Language Models Encode Perceptual Structure Without Grounding? A Case Study in Color](https://md.research.elamur.ai/free/en/papers/abdou-2021-can-language-models-encode-perceptual/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Mostafa Abdou, Artur Kulmizev, Daniel Hershcovich, et al.*2021 · 122 citations*
32. [**Explanations from Large Language Models Make Small Reasoners Better arXiv:2210.06726**Shiyang Li, Jianshu Chen, Yelong Shen, et al.*2022 · 120 citations*](https://md.research.elamur.ai/free/en/papers/li-2022-explanations-large-language-models-make/)
33. [**Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluation arXiv:2212.07981**Yixin Liu, Alexander R. Fabbri, Pengfei Liu, et al.*2022 · 117 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-revisiting-gold-standard-grounding-summarization/)
34. [**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/)
35. [**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/)
36. [**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/)
37. [**Simulating Action Dynamics with Neural Process Networks arXiv:1711.05313**Antoine Bosselut, Omer Levy, Ari Holtzman, et al.*2017 · 102 citations*](https://md.research.elamur.ai/free/en/papers/bosselut-2017-simulating-action-dynamics-neural-process/)
38. [**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/)
39. [**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/)
40. [**What's In My Big Data? arXiv:2310.20707**Yanai Elazar, Akshita Bhagia, Ian Magnusson, et al.*2023 · 92 citations*](https://md.research.elamur.ai/free/en/papers/elazar-2023-s-my-big-data/)
41. [**Dream 7B: Diffusion Large Language Models arXiv:2508.15487**Jiacheng Ye; Zhihui Xie; Lin Zheng; Jiahui Gao; Zirui Wu; Xin Jiang; Zhenguo Li;, Lingpeng Kong*2025 · 89 citations*](https://md.research.elamur.ai/free/en/papers/ye-2025-dream-7b-diffusion-large-language/)
42. **[How to Train Long-Context Language Models (Effectively)](https://md.research.elamur.ai/free/en/papers/gao-2025-how-train-long-context-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tianyu Gao, Alexander Wettig, Howard Yen, et al.*2025 · 89 citations*
43. [**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/)
44. [**Do Membership Inference Attacks Work on Large Language Models? arXiv:2402.07841**Michael Duan, Anshuman Suri, Niloofar Mireshghallah, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/duan-2024-membership-inference-attacks-work-large/)
45. **[Solving Math Word Problems via Cooperative Reasoning induced Language Models](https://md.research.elamur.ai/free/en/papers/zhu-2023-solving-math-word-problems-cooperative/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xinyu Zhu, Junjie Wang, Lin Zhang, et al.*2023 · 86 citations*
46. [**Benchmark Data Contamination of Large Language Models: A Survey arXiv:2406.04244**Cheng Xu, Shuhao Guan, Derek Greene, et al.*2024 · 85 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-benchmark-data-contamination-large/)
47. [**OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction Data arXiv:2410.01560**Shubham Toshniwal, Wei Du, Ivan Moshkov, et al.*2024 · 85 citations*](https://md.research.elamur.ai/free/en/papers/toshniwal-2024-openmathinstruct-2-accelerating-ai-math/)
48. [**BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text arXiv:2403.18421**Elliot Bolton, Abhinav Venigalla, Michihiro Yasunaga, et al.*2024 · 79 citations*](https://md.research.elamur.ai/free/en/papers/bolton-2024-biomedlm-2-7b-parameter-language/)
49. [**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/)
50. [**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/)
51. **[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*
52. [**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/)
53. [**Do Neural Language Representations Learn Physical Commonsense? arXiv:1908.02899**Maxwell Forbes, Ari Holtzman, Yejin Choi*2019 · 64 citations*](https://md.research.elamur.ai/free/en/papers/forbes-2019-do-neural-language-representations-learn/)
54. [**OpenScholar: Synthesizing Scientific Literature with Retrieval-augmented LMs arXiv:2411.14199**Akari Asai, Jacqueline He, Rulin Shao, et al.*2024 · 62 citations*](https://md.research.elamur.ai/free/en/papers/asai-2024-openscholar-synthesizing-scientific-literature/)
55. **[Culturally Aware Natural Language Inference](https://md.research.elamur.ai/free/en/papers/huang-2023-culturally-aware-natural-language-inference/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jing Huang, Diyi Yang*2023 · 61 citations*
56. [**mmBERT: A Modern Multilingual Encoder with Annealed Language Learning arXiv:2509.06888**Marc Marone, Orion Weller, William Fleshman, et al.*2025 · 59 citations*](https://md.research.elamur.ai/free/en/papers/marone-2025-mmbert-modern-multilingual-encoder-annealed/)
57. **[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*
58. [**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/)
59. [**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/)
60. [**Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback arXiv:2303.05453**Hannah Rose Kirk, Bertie Vidgen, Paul Rottger, et al.*2023 · 57 citations*](https://md.research.elamur.ai/free/en/papers/kirk-2023-personalisation-within-bounds-risk-taxonomy/)
61. **[Rethinking Kullback-Leibler Divergence in Knowledge Distillation for Large Language Models](https://md.research.elamur.ai/free/en/papers/wu-2025-rethinking-kullback-leibler-divergence-knowledge/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Taiqiang Wu, Chaofan Tao, Jiahao Wang, et al.*2025 · 47 citations*
62. [**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/)
63. [**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/)
64. [**olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models arXiv:2502.18443**Jake Poznanski, Aman Rangapur, Jon Borchardt, et al.*2025 · 44 citations*](https://md.research.elamur.ai/free/en/papers/poznanski-2025-olmocr-unlocking-trillions-tokens-pdfs/)
65. **[Paloma: A Benchmark for Evaluating Language Model Fit](https://md.research.elamur.ai/free/en/papers/magnusson-2023-paloma-benchmark-evaluating-language-model/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Ian Magnusson, Akshita Bhagia, Valentin Hofmann, et al.*2023 · 42 citations*
66. [**CULTURE-GEN: Revealing Global Cultural Perception in Language Models through Natural Language Prompting arXiv:2404.10199**Huihan Li, Liwei Jiang, Jena D. Hwang, et al.*2024 · 40 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-culture-gen-revealing-global-cultural/)
67. **[Sociodemographic Bias in Language Models: A Survey and Forward Path](https://md.research.elamur.ai/free/en/papers/gupta-2023-sociodemographic-bias-language-models-survey/) [Workshop on Gender Bias in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/gebnlp/)**Vipul Gupta, Pranav Narayanan Venkit, Shomir Wilson, et al.*2023 · 40 citations*
68. [**Introducing v0.5 of the AI Safety Benchmark from MLCommons arXiv:2404.12241**Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed, et al.*2024 · 38 citations*](https://md.research.elamur.ai/free/en/papers/vidgen-2024-introducing-v0-5-ai-safety/)
69. [**On the Diversity of Synthetic Data and its Impact on Training Large Language Models arXiv:2410.15226**Hao Chen, Abdul Waheed, Xiang Li, et al.*2024 · 35 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-diversity-synthetic-data-impact-training/)
70. **[Culture is Not Trivia: Sociocultural Theory for Cultural NLP](https://md.research.elamur.ai/free/en/papers/zhou-2025-culture-is-not-trivia-sociocultural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Naitian Zhou, David Bamman, Isaac L. Bleaman*2025 · 33 citations*
71. **[Probing Contextual Language Models for Common Ground with Visual Representations](https://md.research.elamur.ai/free/en/papers/ilharco-2021-probing-contextual-language-models-common/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Gabriel Ilharco, Rowan Zellers, Ali Farhadi, et al.*2021 · 33 citations*
72. **[Benchmarking Linguistic Diversity of Large Language Models](https://md.research.elamur.ai/free/en/papers/guo-2025-benchmarking-linguistic-diversity-large-language/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Yanzhu Guo, Guokan Shang, Chloé Clavel*2025 · 32 citations*
73. **[Detection and Measurement of Syntactic Templates in Generated Text](https://md.research.elamur.ai/free/en/papers/shaib-2024-detection-measurement-syntactic-templates-generated/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Chantal Shaib, Yanai Elazar, Junyi Jessy Li, et al.*2024 · 32 citations*
74. **[SEA-LION: Southeast Asian Languages in One Network](https://md.research.elamur.ai/free/en/papers/ng-2025-sea-lion-southeast-asian-languages/) [International Joint Conference on Natural Language Processing (IJCNLP)](https://md.research.elamur.ai/free/en/venues/ijcnlp/)**Raymond Ng, Thanh Ngan Nguyen, Huang Yuli, et al.*2025 · 29 citations*
75. **[Towards Effective and Efficient Continual Pre-training of Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-effective-efficient-continual-pre-training/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jie Chen, Zhipeng Chen, Jiapeng Wang, et al.*2025 · 29 citations*
76. **[Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Above](https://md.research.elamur.ai/free/en/papers/balepur-2025-which-these-best-describes-multiple/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Nishant Balepur, Rachel Rudinger, Jordan Boyd-Graber*2025 · 28 citations*
77. [**JetMoE: Reaching Llama2 Performance with 0.1M Dollars arXiv:2404.07413**Yikang Shen, Zhen Guo, Tianle Cai, et al.*2024 · 28 citations*](https://md.research.elamur.ai/free/en/papers/shen-2024-jetmoe-reaching-llama2-performance-0/)
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79. [**RWKV-7 "Goose" with Expressive Dynamic State Evolution arXiv:2503.14456**Bo Peng, Ruichong Zhang, Daniel Goldstein, et al.*2025 · 23 citations*](https://md.research.elamur.ai/free/en/papers/peng-2025-rwkv-7-goose-expressive-dynamic/)
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84. [**Maximize Your Data's Potential: Enhancing LLM Accuracy with Two-Phase Pretraining arXiv:2412.15285**Steven Feng, Shrimai Prabhumoye, Kezhi Kong, et al.*2024 · 12 citations*](https://md.research.elamur.ai/free/en/papers/feng-2024-maximize-data-s-potential-enhancing/)
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88. [**Syntactic Variation Across the Grammar: Modelling a Complex Adaptive System arXiv:2309.11869**Jonathan Dunn*2023 · 7 citations*](https://md.research.elamur.ai/free/en/papers/dunn-2023-syntactic-variation-across-grammar-modelling/)
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100. [**Style Transfer as Bias Mitigation: Diffusion Models for Synthetic Mental Health Text for Arabic arXiv:2601.14124**Saad Mankarious, Aya Zirikly*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/mankarious-2026-style-transfer-bias-mitigation-diffusion/)

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