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title: "Language models & pretraining — papers, page 11"
description: "Language models & pretraining: 1,079 papers of the Elamur Research library · page 11 of 12 · 926–1025, the most cited first."
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# 🧠 Language models & pretraining

1,079 papers · page 11 of 12 · 926–1025 · 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.

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926. [**Contrastively Pre-trained Event Embeddings with Schema-free LLM Annotations**Frank Mtumbuka, Steven Schockaert*2026*](https://md.research.elamur.ai/free/en/papers/mtumbuka-2026-contrastively-pre-trained-event-embeddings/)
927. [**Controllable Sentence Simplification in Italian: Fine-Tuning Large Language Models on Automatically Generated Resources**Michele Papucci, Giulia Venturi, Felice Dell'Orletta*2026*](https://md.research.elamur.ai/free/en/papers/papucci-2026-controllable-sentence-simplification-italian-fine/)
928. [**Crowdsourcing Piedmontese to Test LLMs on Non-Standard Orthography**Gianluca Vico, Jindřich Libovický*2026*](https://md.research.elamur.ai/free/en/papers/vico-2026-crowdsourcing-piedmontese-test-llms-non/)
929. [**DAMETA: An LLM Benchmark for Danish Metaphor Interpretation with Systematically Varied Distractors**Nina Skovgaard Schneidermann, Sanni Nimb, Nathalie Carmen Hau Norman, et al.*2026*](https://md.research.elamur.ai/free/en/papers/schneidermann-2026-dameta-llm-benchmark-danish-metaphor/)
930. [**Detecting Hallucinations in Authentic LLM–Human Interactions**Yujie Ren, Niklas Gruhlke, Anne Lauscher*2026*](https://md.research.elamur.ai/free/en/papers/ren-2026-detecting-hallucinations-authentic-llm-human/)
931. [**Diff4TST: Masked Diffusion Language Model for Text Style Transfer**Xinchen Ma, Gaole He, Yunshi Lan, et al.*2026*](https://md.research.elamur.ai/free/en/papers/ma-2026-diff4tst-masked-diffusion-language-model/)
932. [**Discourse Structure Variation for Large Language Model Evaluation in Text Simplification (大模型文本简化的篇章结构变化分析与评测)**Junhui Zhu (朱君辉), Erhong Yang (杨尔弘), Haonan Zhu (朱浩楠), et al.*2026*](https://md.research.elamur.ai/free/en/papers/zhu-2026-discourse-structure-variation/)
933. [**Do Factual Recall Mechanisms Carry over from Text to Speech in Multimodal Language Models?**Luca Modica, Filip Landin, Mehrdad Farahani, et al.*2026*](https://md.research.elamur.ai/free/en/papers/modica-2026-do-factual-recall-mechanisms-carry/)
934. [**Do LLMs Use Relational Structure in KG-Grounded Dialogue? Evidence from Entity and Relation Perturbation**Dominic Okonkwo, Ismailcem Budak Arpinar*2026*](https://md.research.elamur.ai/free/en/papers/okonkwo-2026-do-llms-use-relational-structure/)
935. [**Do Multimodal LLMs Understand Order? Measuring the Fragility of Multimodal Reasoning under Input Order Perturbations**Sheng-Lun Wei, Yu-Ling Liao, Hen-Hsen Huang, et al.*2026*](https://md.research.elamur.ai/free/en/papers/wei-2026-do-multimodal-llms-understand-order/)
936. [**Dynamic Model Switching to Mitigate Outdated Knowledge in Large Language Models**Ramakrishna Pinninti, Sabyasachi Kamila, Ayan Mazumder, et al.*2026*](https://md.research.elamur.ai/free/en/papers/pinninti-2026-dynamic-model-switching-mitigate-outdated/)
937. [**EL-MIA: Quantifying Membership Inference Risks of Sensitive Entities in LLMs**Ali Satvaty, Suzan Verberne, Fatih Turkmen*2026*](https://md.research.elamur.ai/free/en/papers/satvaty-2026-el-mia-quantifying-membership-inference/)
938. [**Enhancing the Role-playing Capabilities of Reasoning-optimized Large Language Models (推理型大语言模型角色扮演能力评估与优化)**Xiachong Feng (冯夏冲), Yichong Huang (黄毅翀), Xiaocheng Feng (冯骁骋), et al.*2026*](https://md.research.elamur.ai/free/en/papers/feng-2026-enhancing-role-playing/)
939. [**Entropy-aware Masking for Masked Language Modeling**Gokul Srinivasagan, Kai Hartung, Munir Georges*2026*](https://md.research.elamur.ai/free/en/papers/srinivasagan-2026-entropy-aware-masking-masked-language/)
940. [**Evaluating Discriminability of Vision-Language Models**Masayasu Muraoka, Naoaki Okazaki*2026*](https://md.research.elamur.ai/free/en/papers/muraoka-2026-evaluating-discriminability-vision-language-models/)
941. [**Evaluating Humanities Theory Alignment in Large Language Models: Incremental Prompting and Statistical Assessment**Axel Pichler, Janis Pagel*2026*](https://md.research.elamur.ai/free/en/papers/pichler-2026-evaluating-humanities-theory-alignment-large/)
942. [**Evaluating LLM Style Transfer Through Readability-Based Age Assessments**Maria Di Maro, Antonio Origlia, Leonilda Bilo, et al.*2026*](https://md.research.elamur.ai/free/en/papers/maro-2026-evaluating-llm-style-transfer-through/)
943. [**Evaluating Multimodal Large Language Model Narrative Interpretation through the Lens of Appraisal Theory**Jayant Teotia, Xiaowei Wang, Xulang Zhang, et al.*2026*](https://md.research.elamur.ai/free/en/papers/teotia-2026-evaluating-multimodal-large-language-model/)
944. [**Evaluating the Adaptability of Large Language Models to Linguistic Variation**Ziyan Xu, Marina Seghier, Alice Millour, et al.*2026*](https://md.research.elamur.ai/free/en/papers/xu-2026-evaluating-adaptability-large-language-models/)
945. [**Evaluating the Effect of Question Wording Variations on Answer Consistency in Large Language Models**Junya Takayama, Masaya Ohagi, Tomoya Mizumoto, et al.*2026*](https://md.research.elamur.ai/free/en/papers/takayama-2026-evaluating-effect-question-wording-variations/)
946. [**Evaluation Drift in LLM Personality Induction: Are We Moving the Goalpost?**Prateek Kumar Rajput, Yewei Song, Iyiola Emmanuel Olatunji, et al.*2026*](https://md.research.elamur.ai/free/en/papers/rajput-2026-evaluation-drift-llm-personality-induction/)
947. [**Evaluation of Multilingual Text Simplification for the Mental Health Domain: Exploring Small Language Models**Olga Pelloni, Sandra Just, Lars Bongo*2026*](https://md.research.elamur.ai/free/en/papers/pelloni-2026-evaluation-multilingual-text-simplification-mental/)
948. [**Extending Czech Aspect-Based Sentiment Analysis with Opinion Terms: Dataset and LLM Benchmarks**Jakub Šmíd, Pavel Priban, Pavel Kral*2026*](https://md.research.elamur.ai/free/en/papers/smid-2026-extending-czech-aspect-based-sentiment/)
949. [**Facilitating Large Language Model-based Multiple-choice Reading Comprehension with Question-solving Thought (基于解题思维机制的大语言模型多项选择阅读理解增强框架)**Yisong Su (苏艺淞), Yuanzhe Zhang (张元哲), Xiangwen Liao (廖祥文), et al.*2026*](https://md.research.elamur.ai/free/en/papers/su-2026-facilitating-large-language/)
950. [**Few-shot Prompting or Supervised Tuning? A Comparative Study of LLMs for Linguistically Distant Language Pairs in BDI**Deepen Naorem, Sanasam Ranbir Singh, Telem Joyson Singh, et al.*2026*](https://md.research.elamur.ai/free/en/papers/naorem-2026-few-shot-prompting-or-supervised/)
951. [**Fill-in-the-Blanks: Automatic Generation and Evaluation of Language Models' Pseudonyms for English and Swedish Texts**Maria Irena Szawerna, Jacob Lee Suchardt*2026*](https://md.research.elamur.ai/free/en/papers/szawerna-2026-fill-blanks-automatic-generation-evaluation/)
952. [**Finding Sense in Nonsense with Generated Contexts: Perspectives from Humans and Language Models**Katrina Olsen, Sebastian Padó*2026*](https://md.research.elamur.ai/free/en/papers/olsen-2026-finding-sense-nonsense-generated-contexts/)
953. [**Focus particles and scalar inferences across humans and language models**Catherine M. Brousse, Nelu D. Radpour*2026*](https://md.research.elamur.ai/free/en/papers/brousse-2026-focus-particles-scalar-inferences-across/)
954. [**From Perception to Reasoning: Deep Thinking Empowers Multimodal Large Language Models (从感知到推理：深度思考赋能多模态大语言模型)**Wenxin Zhu (祝文鑫), Andong Chen (陈安东), Yuchen Song (宋宇宸), et al.*2026*](https://md.research.elamur.ai/free/en/papers/zhu-2026-perception-reasoning-deep/)
955. [**From Rosetta to Match-Up: A Paired Corpus of Linguistic Puzzles with Human and LLM Benchmarks**Neh Majmudar, Anne Huang, Jinfan Frank Hu, et al.*2026*](https://md.research.elamur.ai/free/en/papers/majmudar-2026-rosetta-match-up-paired-corpus/)
956. [**Generation of Instruction and Preference Dataset for Improving Japanese Instruction Following in LLMs**Kei Moriyama, Takashi Kodama, Kouta Nakayama*2026*](https://md.research.elamur.ai/free/en/papers/moriyama-2026-generation-instruction-preference-dataset-improving/)
957. [**GPT-NL Public Corpus: A Permissively Licensed, Dutch-First Dataset for LLM Pre-training**Jesse J. Van Oort, Frank Brinkkemper, Erik de Graaf, et al.*2026*](https://md.research.elamur.ai/free/en/papers/oort-2026-gpt-nl-public-corpus-permissively/)
958. [**Grounding Arabic LLMs in the Doha Historical Dictionary: Retrieval-Augmented Understanding of Quran and Hadith**Somaya Eltanbouly, Samer Rashwani*2026*](https://md.research.elamur.ai/free/en/papers/eltanbouly-2026-grounding-arabic-llms-doha-historical/)
959. [**HEBATRON: A Hebrew-Specialized Open-Weight Mixture-of-Experts Language Model**Noam Kayzer, Dan Revital, Ori Bar Joseph, et al.*2026*](https://md.research.elamur.ai/free/en/papers/kayzer-2026-hebatron-hebrew-specialized-open-weight/)
960. [**HotelCheckSpan: A Benchmark Dataset for LLM Faithfulness**Patricia Schmidtova, Ondrej Dusek, Saad Mahamood*2026*](https://md.research.elamur.ai/free/en/papers/schmidtova-2026-hotelcheckspan-benchmark-dataset-llm-faithfulness/)
961. [**How Far Can Bias Go? Tracing Bias from Pre-Training Data to Alignment**Marion Thaler, Abdullatif Köksal, Alina Leidinger, et al.*2026*](https://md.research.elamur.ai/free/en/papers/thaler-2026-how-far-can-bias-go/)
962. [**Human vs LLM in Conversational Repair Annotation: A New Resource and Comparative Study**Anh Ngo, Nicolas Rollet, Catherine Pelachaud, et al.*2026*](https://md.research.elamur.ai/free/en/papers/ngo-2026-human-vs-llm-conversational-repair/)
963. [**Human-AI Annotation Error Auditing for Hebrew Diacritization with Frontier LLMs**Hillel Gershuni, Avi Shmidman*2026*](https://md.research.elamur.ai/free/en/papers/gershuni-2026-human-ai-annotation-error-auditing/)
964. [**Identifying Imaging Follow-Up in Radiology Reports: A Comparative Analysis of Traditional ML and LLM Approaches**Namu Park, Giridhar Kaushik Ramachandran, Kevin Lybarger, et al.*2026*](https://md.research.elamur.ai/free/en/papers/park-2026-identifying-imaging-follow-up-radiology/)
965. [**IndigiEval: Evaluating LLMs in North American Indigenous Languages**Julia Mainzinger, Jacqueline Brixey*2026*](https://md.research.elamur.ai/free/en/papers/mainzinger-2026-indigieval-evaluating-llms-north-american/)
966. [**InstructSum: A Benchmark to Evaluate Instruction-Following Capability of Large Language Models in Summarization**Kosuke Nishida, Kyosuke Nishida, Itsumi Saito*2026*](https://md.research.elamur.ai/free/en/papers/nishida-2026-instructsum-benchmark-evaluate-instruction-following/)
967. [**Investigating How LLMs Propagate Female Stereotypes: Comparing What Models Say via Prompts with What They Represent in Their Embeddings**Andrea Valderrey Nuñez, Jelke Bloem*2026*](https://md.research.elamur.ai/free/en/papers/nunez-2026-investigating-how-llms-propagate-female/)
968. [**Investigating Memorization in Language Models Trained via Knowledge Distillation**Maarten Mäcking, Michaela Regneri*2026*](https://md.research.elamur.ai/free/en/papers/macking-2026-investigating-memorization-language-models-trained/)
969. [**Is AI Catching Up to Human Expression? Exploring Emotion, Personality, Authorship, and Linguistic Style in English and Arabic with Six Large Language Models**Nasser A Alsadhan*2026*](https://md.research.elamur.ai/free/en/papers/alsadhan-2026-ai-catching-up-human-expression/)
970. [**Is Biomedical Specialization Still Worth It? Insights from Domain-Adaptive Language Modelling with a New French Health Corpus**Aidan Mannion, Cécile Macaire, Armand Violle, et al.*2026*](https://md.research.elamur.ai/free/en/papers/mannion-2026-is-biomedical-specialization-still-worth/)
971. [**JudgeMeNot: Personalizing Large Language Models to Emulate Judicial Reasoning in Hebrew**Itay Razumenko, Arnon Sturm, Nir Grinberg*2026*](https://md.research.elamur.ai/free/en/papers/razumenko-2026-judgemenot-personalizing-large-language-models/)
972. [**K-CoT: Chinese Parallelism Generation Based on Keywords Chain-of-Thought Prompting (K-CoT：基于关键词思维链提示的中文排比句生成研究)**Maosheng Zhong (钟茂生), Jiaqi Gan (甘家其), Hejun Zhang (张鹤君), et al.*2026*](https://md.research.elamur.ai/free/en/papers/zhong-2026-k-cot-chinese/)
973. [**Large Language Models Put to the Test on Chinese Noun Compounds: Experiments on Natural Language Inference and Compound Semantics**Le Qiu, Emmanuele Chersoni, He Zhou, et al.*2026*](https://md.research.elamur.ai/free/en/papers/qiu-2026-large-language-models-put-test/)
974. [**Leveraging Decoupled Composition in Large Language Models for Knowledge Base Question Answering (基于解耦与组合的大模型知识问答方法)**Shengze Shi (施圣泽), Li Qin (秦利), Jun Hu (胡军), et al.*2026*](https://md.research.elamur.ai/free/en/papers/shi-2026-leveraging-decoupled-composition/)
975. [**LLM as a Morphological Disambiguator for Belarusian: A Preliminary Study**Vladislav Poritski, Oksana Volchek, Ilia Afanasev*2026*](https://md.research.elamur.ai/free/en/papers/poritski-2026-llm-as-morphological-disambiguator-belarusian/)
976. [**LLM-Assisted Spanish Dialect Corpus Construction**Jessica Claribel RAMIREZ VIDAL, Hiroki Ouchi, Sakriani Sakti*2026*](https://md.research.elamur.ai/free/en/papers/vidal-2026-llm-assisted-spanish-dialect-corpus/)
977. [**LLMs as Annotators: Evaluating Model–Human Alignment in Detecting Contentious Language in Historical Corpora**Yahui Zhao, Clemencia Siro, Laura Hollink*2026*](https://md.research.elamur.ai/free/en/papers/zhao-2026-llms-as-annotators-evaluating-model/)
978. [**LLMs Out-of-the-Box Do Not Generate Context-Appropriate Word Order in Russian**Alina Shabaeva, John Frederick Bailyn, Owen Rambow*2026*](https://md.research.elamur.ai/free/en/papers/shabaeva-2026-llms-out-box-do-not/)
979. [**LocRegen: Cost-Efficient Redundancy Removal in Multilingual E-commerce Titles with Small Language Models**Bryan Zhang, Stephan Walter, Luca Lomanto, et al.*2026*](https://md.research.elamur.ai/free/en/papers/zhang-2026-locregen-cost-efficient-redundancy-removal/)
980. [**Low-Rank Compression of Language Models via Differentiable Rank Selection**Sidhant Sundrani, Francesco Tudisco, Pasquale Minervini*2026*](https://md.research.elamur.ai/free/en/papers/sundrani-2026-low-rank-compression-language-models/)
981. [**MASA: A Novel Multimodal Foundation Model for L2 Speaking Assessment in Picture-description Scenarios**Bi-Cheng Yan, Fu-An Chao, Hong-Yun H.Y. Lin, et al.*2026*](https://md.research.elamur.ai/free/en/papers/yan-2026-masa-novel-multimodal-foundation-model/)
982. [**Memorization or Lucky Guesses: Detecting Short Sequences from Copyrighted Dutch News in LLM Output**Joris Veerbeek, Kas Berendsen, Alessandra Polimeno, et al.*2026*](https://md.research.elamur.ai/free/en/papers/veerbeek-2026-memorization-or-lucky-guesses-detecting/)
983. [**Merging Continual Pretraining Models for Domain-Specialized LLMs: A Case Study in Finance**Kentaro Ueda, François Portet, Hirohiko Suwa, et al.*2026*](https://md.research.elamur.ai/free/en/papers/ueda-2026-merging-continual-pretraining-models-domain/)
984. [**MEUR: A Benchmark for Evaluating Vision-Language Models on Multimodal Event Understanding and Reasoning**Zimu Wang, Yuqi Wang, Tong Chen, et al.*2026*](https://md.research.elamur.ai/free/en/papers/wang-2026-meur-benchmark-evaluating-vision-language/)
985. [**Mind the Language Gap: Assessing LLM Safety in Italian**Elena Marafatto, Roberto Navigli*2026*](https://md.research.elamur.ai/free/en/papers/marafatto-2026-mind-language-gap-assessing-llm/)
986. [**Modeling semantic association in self-paced reading with language model embeddings**Sara Møller Østergaard, Kenneth Enevoldsen, Afra Alishahi, et al.*2026*](https://md.research.elamur.ai/free/en/papers/ostergaard-2026-modeling-semantic-association-self-paced/)
987. [**MT-Video-Bench: A Holistic Video Understanding Benchmark for Evaluating Multimodal LLMs in Multi-Turn Dialogues**Yaning Pan, Qianqian Xie, Guohui Zhang, et al.*2026*](https://md.research.elamur.ai/free/en/papers/pan-2026-mt-video-bench-holistic-video/)
988. [**Multi-Task Instruction Tuning via Data Scheduling for Low-Resource Arabic SpeechLLMs**Hunzalah Hassan Bhatti, Firoj Alam, Shammur Absar Chowdhury*2026*](https://md.research.elamur.ai/free/en/papers/bhatti-2026-multi-task-instruction-tuning-data/)
989. [**Multimodal LLMs Do Not Compose Skills Optimally across Modalities**Paula Ontalvilla, Aitor Ormazabal, Gorka Azkune*2026*](https://md.research.elamur.ai/free/en/papers/ontalvilla-2026-multimodal-llms-do-not-compose/)
990. [**MzansiText and MzansiLM: An Open Corpus and Decoder-Only Language Model for South African Languages**Anri M. Lombard, Simbarashe Mawere, Temi Aina, et al.*2026*](https://md.research.elamur.ai/free/en/papers/lombard-2026-mzansitext-mzansilm-open-corpus-decoder/)
991. [**Nuha-Speech: Building General-Purpose Arabic Speech-LLMs**Yingzhi Wang, Reem Alhazzani, Muhammad Alqurishi*2026*](https://md.research.elamur.ai/free/en/papers/wang-2026-nuha-speech-building-general-purpose/)
992. [**On the Use of LLMs for Specialised Terminology: A Good Alternative to Corpora?**Joachim Minder, Guillaume Wisniewski, Natalie Kübler*2026*](https://md.research.elamur.ai/free/en/papers/minder-2026-use-llms-specialised-terminology-good/)
993. [**Onomasiological Sense Alignment Across Dialect Dictionaries. A Taxonomy-Constrained LLM Classification**Nathalie Mederake, Nico Urbach, Hanna Fischer, et al.*2026*](https://md.research.elamur.ai/free/en/papers/mederake-2026-onomasiological-sense-alignment-across-dialect/)
994. [**Open-access Dataset on Acceptability Ratings of Korean Clausal Constructions by Humans and GPT Models**Gyu-Ho Shin, Soo-Hwan Lee, Chanyoung Lee*2026*](https://md.research.elamur.ai/free/en/papers/shin-2026-open-access-dataset-acceptability-ratings/)
995. [**Orthographic Constraint Satisfaction and Human Difficulty Alignment in Large Language Models**Bryan E. Tuck, Rakesh Verma*2026*](https://md.research.elamur.ai/free/en/papers/tuck-2026-orthographic-constraint-satisfaction-human-difficulty/)
996. [**Persona-Aware Evaluation of Cognitive Bias in LLMs: From Benchmark to Applied Decision-Making**Katsumasa Yoshikawa, Junya Takayama, Takato Yamazaki*2026*](https://md.research.elamur.ai/free/en/papers/yoshikawa-2026-persona-aware-evaluation-cognitive-bias/)
997. [**Probing Discrete Speech Tokens of Spoken Language Models**Sven Naber, Julia Koch, Pranav Singh, et al.*2026*](https://md.research.elamur.ai/free/en/papers/naber-2026-probing-discrete-speech-tokens-spoken/)
998. [**Probing the Dynamics of Syntactic Ability Acquisition Throughout LLM Pretraining**Hiroshi Matsuda, Masayuki Asahara*2026*](https://md.research.elamur.ai/free/en/papers/matsuda-2026-probing-dynamics-syntactic-ability-acquisition/)
999. [**Proficiency-Controlled Text Simplification in European Portuguese: A Preliminary Study using Prompting Approaches**Eugénio Ribeiro, David Antunes, Nuno Mamede, et al.*2026*](https://md.research.elamur.ai/free/en/papers/ribeiro-2026-proficiency-controlled-text-simplification-european/)
1000. [**Quantifying Metric and Model Agreement in Bias Evaluation of Large Language Models**Arash Asgari, Huan Wu, Amirreza Naziri, et al.*2026*](https://md.research.elamur.ai/free/en/papers/asgari-2026-quantifying-metric-model-agreement-bias/)
1001. [**Quantifying the Accuracy and Cost Impact of Design Decisions in Budget-Constrained Agentic LLM Search**Kyle A. McCleary, James M. Ghawaly*2026*](https://md.research.elamur.ai/free/en/papers/mccleary-2026-quantifying-accuracy-cost-impact-design/)
1002. [**Questionnaire Meets LLM: A Benchmark and Empirical Study of Structural Skills for Understanding Questions and Responses**Duc-Hai Nguyen, Vijayakumar Nanjappan, Barry O'Sullivan, et al.*2026*](https://md.research.elamur.ai/free/en/papers/nguyen-2026-questionnaire-meets-llm-benchmark-empirical/)
1003. [**R.U.Psycho? A Framework for Robust Unified Psychometric Testing of Language Models**Julian Schelb, Orr Borin, David Garcia, et al.*2026*](https://md.research.elamur.ai/free/en/papers/schelb-2026-r-u-psycho-framework-robust/)
1004. [**Rethinking the Idiomaticity Decomposability Hypothesis: Evidence from Distributional Learning**Maggie Mi, Golzar Atefi, Atsuki Yamaguchi, et al.*2026*](https://md.research.elamur.ai/free/en/papers/mi-2026-rethinking-idiomaticity-decomposability-hypothesis-evidence/)
1005. [**Retrieval-augmented Chain-of-thought Prompting Method for Chinese Frame Semantic Parsing (基于检索增强思维提示的汉语框架语义解析方法)**Yingxu Li (李迎旭), Tao Chen (陈涛), Binyang Li (李斌阳), et al.*2026*](https://md.research.elamur.ai/free/en/papers/li-2026-retrieval-augmented-chain/)
1006. [**Retrieving Responses Useful as References in Counseling via LLM-Generated Structurally Analogous Dialogues**Yu Nakagawa, Nozomu Ikeda, Kotaro Funakoshi, et al.*2026*](https://md.research.elamur.ai/free/en/papers/nakagawa-2026-retrieving-responses-useful-as-references/)
1007. [**SalamahBench: Dialect and Category Level Safety Evaluation of Arabic Language Models**Omar Abdelnasser, Fatemah Alharbi, Khaled Khasawneh, et al.*2026*](https://md.research.elamur.ai/free/en/papers/abdelnasser-2026-salamahbench-dialect-category-level-safety/)
1008. [**SearchLLM: Detecting LLM Paraphrased Text by Measuring the Similarity with Regeneration of the Candidate Source via Search Engine**Hoang-Quoc Nguyen-Son, Minh-Son Dao, Koji Zettsu*2026*](https://md.research.elamur.ai/free/en/papers/son-2026-searchllm-detecting-llm-paraphrased-text/)
1009. [**Seeing the Other Side: Diagnostic Tasks for Viewpoint Reasoning in Vision–Language Models**Makoto Takenaka, Hitomi Yanaka*2026*](https://md.research.elamur.ai/free/en/papers/takenaka-2026-seeing-other-side-diagnostic-tasks/)
1010. [**Semantic Parsing for Evaluating Large Language Models: Separating Linguistic Abilities with YARN**Rémi DE VERGNETTE, Maxime Amblard*2026*](https://md.research.elamur.ai/free/en/papers/vergnette-2026-semantic-parsing-evaluating-large-language/)
1011. [**SemBench: A Universal Semantic Framework for LLM Evaluation**Mikel Zubillaga, Naiara Perez, Oscar Sainz, et al.*2026*](https://md.research.elamur.ai/free/en/papers/zubillaga-2026-sembench-universal-semantic-framework-llm/)
1012. [**SemToken: Semantic-Aware Tokenization for Efficient Long-Context Language Models**Dong Liu, Yanxuan Yu*2026*](https://md.research.elamur.ai/free/en/papers/liu-2026-semtoken-semantic-aware-tokenization-efficient/)
1013. [**Sentiment Analysis and Language Models for Kwanyama**Ndapa Nakashole*2026*](https://md.research.elamur.ai/free/en/papers/nakashole-2026-sentiment-analysis-language-models-kwanyama/)
1014. [**Sentiment Classification of Gaza War Headlines: A Comparative Analysis of Large Language Models and Arabic Fine-Tuned BERT Models**Amr Eleraqi; Hager H. Mustafa;, Abdul Hadi N. Ahmed*2026*](https://md.research.elamur.ai/free/en/papers/eleraqi-2026-sentiment-classification-gaza-war-headlines/)
1015. [**Similar Predictions, Different Processes: A Multi-Level Comparison of Human and Multimodal LLM Language Prediction**Shuqi Wang, Zhenguang Cai*2026*](https://md.research.elamur.ai/free/en/papers/wang-2026-similar-predictions-different-processes-multi/)
1016. [**Simulating Student Interactions for Virtual Pretesting with In-Context Learning**Arthur Thuy, Luca Benedetto, Ekaterina Loginova, et al.*2026*](https://md.research.elamur.ai/free/en/papers/thuy-2026-simulating-student-interactions-virtual-pretesting/)
1017. [**SlovKE: A Large-Scale Dataset and LLM Evaluation for Slovak Keyphrase Extraction**Dávid Števaňák, Marek Suppa*2026*](https://md.research.elamur.ai/free/en/papers/stevanak-2026-slovke-large-scale-dataset-llm/)
1018. [**Small Language Models for Less-Resourced Languages in a Real-World Scenario: The Case for Catalan**Roser Saurí, Josep Sànchez-Ferreres, Lluis Padro, et al.*2026*](https://md.research.elamur.ai/free/en/papers/sauri-2026-small-language-models-less-resourced/)
1019. [**Some Dialects Are More Equal Than Others: Non-Prestigious Arabic Dialectal Bias in LLMs**Mai Mohamed Eida, Ryan Dolan, Paul de Nijs, et al.*2026*](https://md.research.elamur.ai/free/en/papers/eida-2026-some-dialects-more-equal-than/)
1020. [**SPQ: An Ensemble Technique for Large Language Model Compression**Jiamin Yao, Eren Gultepe*2026*](https://md.research.elamur.ai/free/en/papers/yao-2026-spq-ensemble-technique-large-language/)
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1022. [**STRUDEL: Unrolling a Benchmark for Evaluating Vision-Language Models on Structured Diagram Understanding across Domains**Daniel Steinigen, Lucie Flek, Sebastian Houben*2026*](https://md.research.elamur.ai/free/en/papers/steinigen-2026-strudel-unrolling-benchmark-evaluating-vision/)
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1024. [**Survey on KV Cache Compression Methods for Large Language Model Inference (大语言模型推理中的键值缓存压缩方法综述)**Shipeng Hu (胡世鹏), Guangyan Zhang (张广艳), Weimin Zheng (郑纬民)*2026*](https://md.research.elamur.ai/free/en/papers/hu-2026-kv-cache-compression/)
1025. [**Syntactic Priming in Few-Shot Learning: How Demonstration Structure Shapes LLM Performance**Prasanth Yadla*2026*](https://md.research.elamur.ai/free/en/papers/yadla-2026-syntactic-priming-few-shot-learning/)

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