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title: "Language models & pretraining — papers, page 12"
description: "Language models & pretraining: 1,079 papers of the Elamur Research library · page 12 of 12 · 1026–1079, the most cited first."
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# 🧠 Language models & pretraining

1,079 papers · page 12 of 12 · 1026–1079 · the most cited first · [all topics](https://md.research.elamur.ai/free/en/topics/)

Models that learn a language from raw text, and how they are pretrained, tuned and prompted.

[← prev](https://md.research.elamur.ai/free/en/topics/language-models/page/11/) [1](https://md.research.elamur.ai/free/en/topics/language-models/) … [11](https://md.research.elamur.ai/free/en/topics/language-models/page/11/) **12**

1026. [**Synthetic Instruction Generation for Low-Resource Nordic Languages: Viability and Limitations in LLM Instruction-Tuning**Mathias Stenlund, Annika Simonsen, Lars Bungum, et al.*2026*](https://md.research.elamur.ai/free/en/papers/stenlund-2026-synthetic-instruction-generation-low-resource/)
1027. [**Teaching LLMs to unveil tendentious implicit contents of Italian political communication**Walter Paci, Lorenzo Gregori, Alessandro Panunzi*2026*](https://md.research.elamur.ai/free/en/papers/paci-2026-teaching-llms-unveil-tendentious-implicit/)
1028. [**The Frequency Confound in Language-Model Surprisal and Metaphor Novelty**Omar Momen, Sina Zarrieß*2026*](https://md.research.elamur.ai/free/en/papers/momen-2026-frequency-confound-language-model-surprisal/)
1029. [**The Impact of Tokenization Algorithms on Hungarian Language Model Performance**Mátyás Osváth, Máté Norbert Molnár, Roland Gunics, et al.*2026*](https://md.research.elamur.ai/free/en/papers/osvath-2026-impact-tokenization-algorithms-hungarian-language/)
1030. [**The Mark Fades: Adaptive Evolutionary Paraphrase-based Attack against LLM Watermarks**Yusheng Zhao, Jian Zhao, Tianle Zhang, et al.*2026*](https://md.research.elamur.ai/free/en/papers/zhao-2026-mark-fades-adaptive-evolutionary-paraphrase/)
1031. [**Toward Cognitive Alignment in Large Language Models: Integrating Linguistic Theory and Human Data**Wajdi Zaghouani*2026*](https://md.research.elamur.ai/free/en/papers/zaghouani-2026-cognitive-alignment-large-language-models/)
1032. [**Toxic Subword Pruning for Dialogue Response Generation on Large Language Models**Hongyuan Adam Lu, Wai Lam*2026*](https://md.research.elamur.ai/free/en/papers/lu-2026-toxic-subword-pruning-dialogue-response/)
1033. [**TryggLLM: A Benchmark for Evaluating LLM Safety in Norwegian**Samia Touileb, Truls Pedersen, Isabell Stinessen Haugen*2026*](https://md.research.elamur.ai/free/en/papers/touileb-2026-tryggllm-benchmark-evaluating-llm-safety/)
1034. [**Uncovering Hidden Violent Tendencies in LLMs: A Demographic Analysis via Behavioral Vignettes**Quintin Myers, Yanjun Gao*2026*](https://md.research.elamur.ai/free/en/papers/myers-2026-uncovering-hidden-violent-tendencies-llms/)
1035. [**ViX-Ray: A Vietnamese Chest X-Ray Dataset for Vision-Language Models**Duy Vu Minh Nguyen, Chinh Thanh Truong, Trần Hoàng Phúc, et al.*2026*](https://md.research.elamur.ai/free/en/papers/nguyen-2026-vix-ray-vietnamese-chest-x/)
1036. [**What Are LLMs Doing to Scientific Communication? Measuring Changes in Writing Practices and Reading Experience**Filip Miletić, Neele Falk*2026*](https://md.research.elamur.ai/free/en/papers/miletic-2026-what-are-llms-doing-scientific/)
1037. [**What Brain Data Adds to Language Model Training**Gabriele Merlin, Omer Moussa, Mariya Toneva*2026*](https://md.research.elamur.ai/free/en/papers/merlin-2026-what-brain-data-adds-language/)
1038. [**What Kind of Language is Easy to Language-Model Under Curriculum Learning?**Nadine El-Naggar, Tatsuki Kuribayashi, Ted Briscoe*2026*](https://md.research.elamur.ai/free/en/papers/naggar-2026-what-kind-language-is-easy/)
1039. [**When Similar Means Different: Evaluating LLMs on Arabic–Hebrew Cognates**Junhong Liang, Noor Abo Mokh, Bashar Alhafni*2026*](https://md.research.elamur.ai/free/en/papers/liang-2026-similar-means-different-evaluating-llms/)
1040. [**Which Way Does Time Flow? A Psychophysics-Grounded Evaluation for Vision–Language Models**Shiho Matta, Lis Kanashiro Pereira, Peitao Han, et al.*2026*](https://md.research.elamur.ai/free/en/papers/matta-2026-which-way-does-time-flow/)
1041. [**Word predictability estimates from language models are not robust to tokenizer vocabulary**Kien Nguyen, Suhas Arehalli*2026*](https://md.research.elamur.ai/free/en/papers/nguyen-2026-word-predictability-estimates-language-models/)
1042. [**YallaMorph: A Benchmark for Evaluating Arabic Morphological Generation in Large Language Models**Mahmoud Reda, Salam Khalifa, Reham Marzouk, et al.*2026*](https://md.research.elamur.ai/free/en/papers/reda-2026-yallamorph-benchmark-evaluating-arabic-morphological/)
1043. [**A Synergetic LLM-KG Framework for Cross-Domain Heterogeneous Data Query (大语言模型和知识图谱协同的跨域异质数据查询框架)**Wenlong Wu (吴文隆), Hailian Yin (尹海莲), Ning Wang (王宁), et al.*2025*](https://md.research.elamur.ai/free/en/papers/wu-2025-synergetic-llm-kg/)
1044. [**An Empirical Study of Chinese Entity Linking Based on Large Language Model (基于大语言模型的中文实体链接实证研究)**Zhengfei Xu (徐正斐), Xin Xin (辛欣)*2025*](https://md.research.elamur.ai/free/en/papers/xu-2025-empirical-study-chinese/)
1045. [**Assessing the Agreement Competence of Large Language Models**Alba Táboas García, Leo Wanner*2025*](https://md.research.elamur.ai/free/en/papers/garcia-2025-assessing-agreement-competence-large-language/)
1046. [**Dhati+: Fine-tuned Large Language Models for Arabic Subjectivity Evaluation**Slimane Bellaouar, Attia Nehar, Soumia Souffi, et al.*2025*](https://md.research.elamur.ai/free/en/papers/bellaouar-2025-dhati-fine-tuned-large-language/)
1047. [**Domain-Adaptive Pre-Training for Arabic Aspect-Based Sentiment Analysis: A Comparative Study of Domain Adaptation and Fine-Tuning Strategies**Salha Alyami, Amani Jamal, Areej Alhothali*2025*](https://md.research.elamur.ai/free/en/papers/alyami-2025-domain-adaptive-pre-training-arabic/)
1048. [**Evaluation on Chinese Multiturn Instruction Following Capability of Large Language Models (大语言模型的中文多轮指令遵循能力评测研究)**Qin Zhu (朱秦), Junliang He (何俊亮), Xipeng Qiu (邱锡鹏), et al.*2025*](https://md.research.elamur.ai/free/en/papers/zhu-2025-evaluation-chinese-multiturn/)
1049. [**Fine Tuning Methods for Large Language Models: A Survey (大语言模型微调方法研究综述)**Chunzhi Wu (吴春志), Yulong Zhao (赵玉龙), Xin Liu (刘鑫), et al.*2025*](https://md.research.elamur.ai/free/en/papers/wu-2025-fine-tuning-methods/)
1050. [**From Human Preference to Autonomous Alignment: A Survey of Large Language Model Alignment (从人类偏好到自主对齐: 大语言模型对齐方法综述)**Shihan Dou (窦士涵), Ming Zhang (张明), Xuanjing Huang (黄萱菁), et al.*2025*](https://md.research.elamur.ai/free/en/papers/dou-2025-human-preference-autonomous/)
1051. [**Huaputong: Large Language Model for Genealogical Question-answering with Knowledge Reasoning (华谱通: 基于知识推理的家谱问答大语言模型)**Xindong Wu (吴信东), Xingrui Zhuo (卓兴锐), Yongpan Chang (常永泮), et al.*2025*](https://md.research.elamur.ai/free/en/papers/wu-2025-huaputong-large-language/)
1052. [**Is It JUST Semantics? A Case Study of Discourse Particle Understanding in LLMs**William Sheffield, Kanishka Misra, Valentina Pyatkin, et al.*2025*](https://md.research.elamur.ai/free/en/papers/sheffield-2025-is-it-textit-just-semantics/)
1053. [**LingLong: A High-Quality Small-Scale Chinese Pre-trained Language Model (玲珑：一个小规模的高质量中文预训练语言模型)**Dongwen Li (李东闻), Zhenyu Zhong (钟震宇), Yufei Sun (孙羽菲), et al.*2025*](https://md.research.elamur.ai/free/en/papers/li-2025-linglong-high-quality/)
1054. [**Mubeen AI: A Specialized Arabic Language Model for Heritage Preservation and User Intent Understanding**Mohammed Aljafari, Ismail Alturki, Ahmed Mori, et al.*2025*](https://md.research.elamur.ai/free/en/papers/aljafari-2025-mubeen-ai-specialized-arabic-language/)
1055. [**Self-Guide: Enhancing LLM Reasoning Ability via Self-Planning (Self-Guide:一种基于自我规划的大语言模型推理增强方法)**Yibin Liu (刘艺彬), Zhenghao Liu (刘正皓), Yukun Yan (闫宇坤), et al.*2025*](https://md.research.elamur.ai/free/en/papers/liu-2025-self-guide-enhancing/)
1056. [**Survey of Adversarial Attacks and Defenses for Large Language Models (大语言模型对抗性攻击与防御综述)**Jianwei Tai (台建玮), Shuangning Yang (杨双宁), Jiajia Wang (王佳佳), et al.*2025*](https://md.research.elamur.ai/free/en/papers/tai-2025-adversarial-attacks-defenses/)
1057. [**Survey of Large-Language-Model-Based Automated Program Repair (基于大语言模型的自动代码修复综述)**Pengyu Xu (许鹏宇), Boyu Kuang (况博裕), Mang Su (苏铓), et al.*2025*](https://md.research.elamur.ai/free/en/papers/xu-2025-large-language-model/)
1058. [**Survey of Storage Optimization Techniques in Large Language Model Inference (大语言模型推理中的存储优化技术综述)**Xuran Ge (葛旭冉), Yang Ou (欧洋), Bo Wang (王博), et al.*2025*](https://md.research.elamur.ai/free/en/papers/ge-2025-storage-optimization-techniques/)
1059. [**Survey of Tool Use in Large Language Models (大语言模型的工具使用综述)**Yining Zheng (郑逸宁), Zhen Yu (余镇), Bufan Li (李不凡), et al.*2025*](https://md.research.elamur.ai/free/en/papers/zheng-2025-tool-use-large/)
1060. [**Survey on Key Technologies for Large Language Model Pre-training Systems (大语言模型预训练系统关键技术综述)**Yanjie Gao (高彦杰), Yueguo Chen (陈跃国)*2025*](https://md.research.elamur.ai/free/en/papers/gao-2025-key-technologies-large/)
1061. [**A Survey of Chinese Large Language Models in Medicine: Progress, Evaluation and Challenges (中文医疗大模型综述: 进展、评估与挑战)**Xinmeng Ji (籍欣萌), Hongying Zan (昝红英), Tingting Cui (崔婷婷), et al.*2024*](https://md.research.elamur.ai/free/en/papers/ji-2024-chinese-large-language/)
1062. [**Building and evaluating a Japanese-English bilingual model by vocabulary-replacing continual pretraining (語彙置換継続事前学習による日英バイリンガルモデルの構築と評価)**麻場直喜, 野崎雄太, 中島大, et al.*2024*](https://md.research.elamur.ai/free/en/papers/asaba-2024-building-evaluating-japanese-english/)
1063. [**Complexity factors of language models in reading comprehension: a controlled experiment (Étude des facteurs de complexité des modèles de langage dans une tâche de compréhension de lecture à l'aide d'une expérience contrôlée sémantiquement)**Elie Antoine, Frederic Bechet, Géraldine Damnati, et al.*2024*](https://md.research.elamur.ai/free/en/papers/antoine-2024-etude-des-facteurs-de-complexite/)
1064. [**Dynamic changes in search strategy during chain-of-thought reasoning of language models (言語モデルの思考連鎖的推論における探索戦略の動的変化)**青木洋一, 工藤慧音, 曾根周作, et al.*2024*](https://md.research.elamur.ai/free/en/papers/aoki-2024-dynamic-changes-search-strategy/)
1065. [**Evaluating Large Language Models: A Survey of Research Progress (大语言模型评测综述)**Wen Luo (罗文), Houfeng Wang (王厚峰)*2024*](https://md.research.elamur.ai/free/en/papers/luo-2024-evaluating-large-language/)
1066. [**Exploring Chinese Pre-Training with Mixed-Grained Encoding and IDF-Masking (探索中文预训练模型的混合粒度编码和IDF遮蔽)**Yunfan Shao (邵云帆), Tianxiang Sun (孙天祥), Xipeng Qiu (邱锡鹏)*2024*](https://md.research.elamur.ai/free/en/papers/shao-2024-exploring-chinese-pre/)
1067. [**Jailbreak Attack for Large Language Models: A Survey (面向大语言模型的越狱攻击综述)**Nan Li (李南), Yidong Ding (丁益东), Haoyu Jiang (江浩宇), et al.*2024*](https://md.research.elamur.ai/free/en/papers/li-2024-jailbreak-attack-large/)
1068. [**Large Language Models and Biblical Hebrew: Limitations, pitfalls, opportunities**Camil Staps*2024*](https://md.research.elamur.ai/free/en/papers/staps-2024-large-language-models-biblical-hebrew/)
1069. [**LLMs as Compiler for Arabic Programming Language**Serry Sibaee, Omar Najar, Lahouri Ghouti, et al.*2024*](https://md.research.elamur.ai/free/en/papers/sibaee-2024-llms-compiler-arabic-programming-language/)
1070. [**Method for Expanding Event Commonsense Knowledge Graph Based on Large Language Models (基于大语言模型的事件常识知识图谱扩展方法)**Qiaojuan Huang (黄俏娟), Cungen Cao (曹存根), Ya Wang (王亚), et al.*2024*](https://md.research.elamur.ai/free/en/papers/huang-2024-method-expanding-event/)
1071. [**On the importance of Data Scale in Pretraining Arabic Language Models**Abbas Ghaddar, Philippe Langlais, Mehdi Rezagholizadeh, et al.*2024*](https://md.research.elamur.ai/free/en/papers/ghaddar-2024-importance-data-scale-pretraining-arabic/)
1072. [**Research Progress of Video Question Answering Technologies (视频问答技术研究进展)**Cuizhu Bao (包翠竹), Kai Ding (丁凯), Jianfeng Dong (董建锋), et al.*2024*](https://md.research.elamur.ai/free/en/papers/bao-2024-video-question-answering/)
1073. [**Robustness of GPT Large Language Models on Natural Language Processing Tasks (GPT系列大语言模型在自然语言处理任务中的鲁棒性)**Xuanting Chen (陈炫婷), Junjie Ye (叶俊杰), Can Zu (祖璨), et al.*2024*](https://md.research.elamur.ai/free/en/papers/chen-2024-robustness-gpt-large/)
1074. [**Survey on Hallucinations in Large Language Models (大语言模型的幻觉问题研究综述)**Zeyuan Liu (刘泽垣), Pengjiang Wang (王鹏江), Xiaobin Song (宋晓斌), et al.*2024*](https://md.research.elamur.ai/free/en/papers/liu-2024-hallucinations-large-language/)
1075. [**Temporal Knowledge Graph Forecasting with Story-Inspired Large Language Models (故事启发大语言模型的时序知识图谱预测)**Juan Chen (陈娟), Xinchao Zhao (赵新潮), Jingyan Sui (隋京言), et al.*2024*](https://md.research.elamur.ai/free/en/papers/chen-2024-temporal-knowledge-graph/)
1076. [**Construction of Chinese Sentence-Level Gender-Unbiased Data Set and Evaluation of Gender Bias in Pre-Training Language Model (中文句子级性别无偏数据集构建及预训练语言模型的性别偏度评估)**Jishun Zhao (赵继舜), Bingjie Du (杜冰洁), Pengyuan Liu (刘鹏远), et al.*2023*](https://md.research.elamur.ai/free/en/papers/zhao-2023-construction-chinese-sentence/)
1077. [**Language Model Enhanced Chinese Relation Extraction (基于语言模型增强的中文关系抽取方法)**Ping Xue (薛平), Ying Li (李影), Zhonghai Wu (吴中海)*2023*](https://md.research.elamur.ai/free/en/papers/xue-2023-language-model-enhanced/)
1078. [**Leveraging BERT Language Model for Arabic Long Document Classification**Muhammad AL-Qurishi*2023*](https://md.research.elamur.ai/free/en/papers/al-qurishi-2023-leveraging-bert-language-model-arabic/)
1079. [**Survey on Vision-language Pre-training (视觉语言预训练综述)**Jiong Yin (殷炯), Zhedong Zhang (张哲东), Yuhan Gao (高宇涵), et al.*2022*](https://md.research.elamur.ai/free/en/papers/yin-2022-vision-language-pre/)

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