---
title: "Who cites Xiang Lorraine Li — page 3 — 927 citing papers"
description: "927 papers of the Elamur Research library cite the works of Xiang Lorraine Li, found in their bibliographies · page 3 of 4 · 201–300; the most…"
canonical: "https://research.elamur.ai/free/en/authors/xiang-lorraine-li/cited-by/page/3/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/xiang-lorraine-li/cited-by/page/3/)

[home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Xiang Lorraine Li](https://md.research.elamur.ai/free/en/authors/xiang-lorraine-li/) / [cited by](https://md.research.elamur.ai/free/en/authors/xiang-lorraine-li/cited-by/) / page 3

# Who cites Xiang Lorraine Li

937 citations of their works from 927 papers of our library (of the 149,439 whose bibliographies we read) · 301 with a page here, the most cited first · page 3 of 4 · 201–300 · [← Xiang Lorraine Li](https://md.research.elamur.ai/free/en/authors/xiang-lorraine-li/)

[← prev](https://md.research.elamur.ai/free/en/authors/xiang-lorraine-li/cited-by/page/2/) [1](https://md.research.elamur.ai/free/en/authors/xiang-lorraine-li/cited-by/) [2](https://md.research.elamur.ai/free/en/authors/xiang-lorraine-li/cited-by/page/2/) **3** [4](https://md.research.elamur.ai/free/en/authors/xiang-lorraine-li/cited-by/page/4/) [next →](https://md.research.elamur.ai/free/en/authors/xiang-lorraine-li/cited-by/page/4/)

201. [**The Generative AI Paradox: "What It Can Create, It May Not Understand" arXiv:2311.00059**Peter West, Ximing Lu, Nouha Dziri, et al.*2023 · 62 citations*](https://md.research.elamur.ai/free/en/papers/west-2023-generative-ai-paradox-create-may/)
202. [**ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning arXiv:2502.01100**Bill Yuchen Lin, Ronan Le Bras, Kyle Richardson, et al.*2025 · 60 citations*](https://md.research.elamur.ai/free/en/papers/lin-2025-zebralogic-scaling-limits-llms-logical/)
203. **[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*
204. [**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/)
205. [**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/)
206. [**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/)
207. **[Hopping Too Late: Exploring the Limitations of Large Language Models on Multi-Hop Queries](https://md.research.elamur.ai/free/en/papers/biran-2024-hopping-too-late-exploring-limitations/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Eden Biran, Daniela Gottesman, Sohee Yang, et al.*2024 · 55 citations*
208. [**Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models — A Survey arXiv:2404.01869**Philipp Mondorf, Barbara Plank*2024 · 53 citations*](https://md.research.elamur.ai/free/en/papers/mondorf-2024-accuracy-evaluating-reasoning-behavior-large/)
209. [**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/)
210. [**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/)
211. [**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/)
212. [**MiniMax-01: Scaling Foundation Models with Lightning Attention arXiv:2501.08313**MiniMax, Aonian Li, Bangwei Gong, et al.*2025 · 50 citations*](https://md.research.elamur.ai/free/en/papers/minimax-2025-minimax-01-scaling-foundation/)
213. **[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*
214. [**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/)
215. [**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/)
216. **[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*
217. [**EuroLLM-9B: Technical Report arXiv:2506.04079**Pedro Henrique Martins, Joao Alves, Patrick Fernandes, et al.*2025 · 45 citations*](https://md.research.elamur.ai/free/en/papers/martins-2025-eurollm-9b-technical-report/)
218. **[On Memorization of Large Language Models in Logical Reasoning](https://md.research.elamur.ai/free/en/papers/xie-2025-memorization-large-language-models-logical/) [International Joint Conference on Natural Language Processing (IJCNLP)](https://md.research.elamur.ai/free/en/venues/ijcnlp/)**Chulin Xie, Yangsibo Huang, Chiyuan Zhang, et al.*2025 · 45 citations*
219. [**Contrastive Chain-of-Thought Prompting arXiv:2311.09277**Yew Ken Chia, Guizhen Chen, Luu Anh Tuan, et al.*2023 · 45 citations*](https://md.research.elamur.ai/free/en/papers/chia-2023-contrastive-chain-thought-prompting/)
220. [**HyperCLOVA X Technical Report arXiv:2404.01954**Kang Min Yoo, Jaegeun Han, Sookyo In, et al.*2024 · 44 citations*](https://md.research.elamur.ai/free/en/papers/yoo-2024-hyperclova-x-technical-report/)
221. **[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*
222. [**Transcending Scaling Laws with 0.1% Extra Compute arXiv:2210.11399**Yi Tay, Jason Wei, Hyung Won Chung, et al.*2022 · 42 citations*](https://md.research.elamur.ai/free/en/papers/tay-2022-transcending-scaling-laws-0-1/)
223. [**DeepSeek: Paradigm Shifts and Technical Evolution in Large AI Models arXiv:2507.09955**Luolin Xiong, Haofen Wang, Xi Chen, et al.*2025 · 41 citations*](https://md.research.elamur.ai/free/en/papers/xiong-2025-deepseek-paradigm-shifts-technical/)
224. [**The MiniPile Challenge for Data-Efficient Language Models arXiv:2304.08442**Jean Kaddour*2023 · 41 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-minipile-challenge-data-efficient-language/)
225. [**PanGu-{\Sigma}: Towards Trillion Parameter Language Model with Sparse Heterogeneous Computing arXiv:2303.10845**Xiaozhe Ren, Pingyi Zhou, Xinfan Meng, et al.*2023 · 40 citations*](https://md.research.elamur.ai/free/en/papers/ren-2023-pangu-sigma-trillion-parameter/)
226. **[Reasoning Abilities of Large Language Models: In-Depth Analysis on the Abstraction and Reasoning Corpus](https://md.research.elamur.ai/free/en/papers/lee-2024-reasoning-abilities-large-language-models/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Seungpil Lee, Woochang Sim, Donghyeon Shin, et al.*2024 · 38 citations*
227. **[JASMINE: Arabic GPT Models for Few-Shot Learning](https://md.research.elamur.ai/free/en/papers/nagoudi-2023-jasmine-arabic-gpt-models-few/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**El Moatez Billah Nagoudi, Muhammad Abdul-Mageed, AbdelRahim Elmadany, et al.*2023 · 38 citations*
228. [**SlimPajama-DC: Understanding Data Combinations for LLM Training arXiv:2309.10818**Zhiqiang Shen, Tianhua Tao, Liqun Ma, et al.*2023 · 38 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-slimpajama-dc-understanding-data-combinations/)
229. [**Impossible Distillation: from Low-Quality Model to High-Quality Dataset & Model for Summarization and Paraphrasing arXiv:2305.16635**Jaehun Jung, Peter West, Liwei Jiang, et al.*2023 · 36 citations*](https://md.research.elamur.ai/free/en/papers/jung-2023-impossible-distillation-low-quality-model/)
230. [**M3KE: A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language Models arXiv:2305.10263**Chuang Liu, Renren Jin, Yuqi Ren, et al.*2023 · 34 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-m3ke-massive-multi-level/)
231. [**TransNormerLLM: A Faster and Better Large Language Model with Improved TransNormer arXiv:2307.14995**Zhen Qin, Dong Li, Weigao Sun, et al.*2023 · 33 citations*](https://md.research.elamur.ai/free/en/papers/qin-2023-transnormerllm-faster-better-large/)
232. [**A Survey of Small Language Models arXiv:2410.20011**Chien Van Nguyen, Xuan Shen, Ryan Aponte, et al.*2024 · 31 citations*](https://md.research.elamur.ai/free/en/papers/nguyen-2024-survey-small-language-models/)
233. **[Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations](https://md.research.elamur.ai/free/en/papers/hagele-2024-scaling-laws-compute-optimal-training/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Alexander Hägele, Elie Bakouch, Atli Kosson, et al.*2024 · 31 citations*
234. [**Nemotron-4 15B Technical Report arXiv:2402.16819**Jupinder Parmar, Shrimai Prabhumoye, Joseph Jennings, et al.*2024 · 30 citations*](https://md.research.elamur.ai/free/en/papers/parmar-2024-nemotron-4-15b-technical-report/)
235. **[A Holistic Assessment of the Carbon Footprint of Noor, a Very Large Arabic Language Model](https://md.research.elamur.ai/free/en/papers/lakim-2022-holistic-assessment-carbon-footprint-noor/) [BigScience Episode – Workshop on Challenges & Perspectives in Creating Large Language Models](https://md.research.elamur.ai/free/en/venues/bigscience/)**Imad Lakim, Ebtesam Almazrouei, Ibrahim Abualhaol, et al.*2022 · 30 citations*
236. **[Can Large Language Models Unlock Novel Scientific Research Ideas?](https://md.research.elamur.ai/free/en/papers/kumar-2025-can-large-language-models-unlock/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Sandeep Kumar, Tirthankar Ghosal, Vinayak Goyal, et al.*2025 · 27 citations*
237. **[Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs](https://md.research.elamur.ai/free/en/papers/chen-2023-two-failures-self-consistency-multi/) [Transactions on Machine Learning Research](https://md.research.elamur.ai/free/en/venues/transactions-on-machine-learning-research/)**Angelica Chen, Jason Phang, Alicia Parrish, et al.*2023 · 25 citations*
238. [**Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model arXiv:2404.04167**Xinrun Du; Zhouliang Yu; Songyang Gao; Ding Pan; Yuyang Cheng; Ziyang Ma; Ruibin Yuan; Xingwei Qu; Jiaheng Liu; Tianyu Zheng; Xinchen Luo; Guorui Zhou; Wenhu Chen;, Ge Zhang*2024 · 23 citations*](https://md.research.elamur.ai/free/en/papers/du-2024-chinese-tiny-llm-pretraining/)
239. **[Compositionality and Sentence Meaning: Comparing Semantic Parsing and Transformers on a Challenging Sentence Similarity Dataset](https://md.research.elamur.ai/free/en/papers/fodor-2025-compositionality-sentence-meaning-comparing-semantic/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**James Fodor, Simon De Deyne, Shinsuke Suzuki*2025 · 22 citations*
240. [**InstructAlign: High-and-Low Resource Language Alignment via Continual Crosslingual Instruction Tuning arXiv:2305.13627**Samuel Cahyawijaya, Holy Lovenia, Tiezheng Yu, et al.*2023 · 22 citations*](https://md.research.elamur.ai/free/en/papers/cahyawijaya-2023-instructalign-high-low-resource-language/)
241. **[AraMUS: Pushing the Limits of Data and Model Scale for Arabic Natural Language Processing](https://md.research.elamur.ai/free/en/papers/alghamdi-2023-aramus-pushing-limits-data-model/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Asaad Alghamdi, Xinyu Duan, Wei Jiang, et al.*2023 · 21 citations*
242. [**DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules**Yanchen Liu, William Held, Diyi Yang*2023 · 20 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-dada-dialect-adaptation-dynamic-aggregation/)
243. **[Resolving Discrepancies in Compute-Optimal Scaling of Language Models](https://md.research.elamur.ai/free/en/papers/porian-2024-resolving-discrepancies-compute-optimal-scaling/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Tomer Porian, Mitchell Wortsman, Jenia Jitsev, et al.*2024 · 19 citations*
244. **[DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning Graph](https://md.research.elamur.ai/free/en/papers/zhang-2024-darg-dynamic-evaluation-large-language/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhehao Zhang, Jiaao Chen, Diyi Yang*2024 · 17 citations*
245. [**Skills-in-Context Prompting: Unlocking Compositionality in Large Language Models arXiv:2308.00304**Jiaao Chen, Xiaoman Pan, Dian Yu, et al.*2023 · 17 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-skills-context-prompting-unlocking-compositionality/)
246. **[Towards Data Contamination Detection for Modern Large Language Models: Limitations, Inconsistencies, and Oracle Challenges](https://md.research.elamur.ai/free/en/papers/samuel-2025-data-contamination-detection-modern-large/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Vinay Samuel, Yue Zhou, Henry Peng Zou*2025 · 16 citations*
247. **[Language Models Encode the Value of Numbers Linearly](https://md.research.elamur.ai/free/en/papers/zhu-2025-language-models-encode-value-numbers/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Fangwei Zhu, Damai Dai, Zhifang Sui*2025 · 15 citations*
248. [**Rethinking Reflection in Pre-Training arXiv:2504.04022**Essential AI: Darsh J Shah, Peter Rushton, Somanshu Singla, et al.*2025 · 15 citations*](https://md.research.elamur.ai/free/en/papers/shah-2025-rethinking-reflection-pre-training/)
249. **[AboutMe: Using Self-Descriptions in Webpages to Document the Effects of English Pretraining Data Filters](https://md.research.elamur.ai/free/en/papers/lucy-2024-aboutme-self-descriptions-webpages-document/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Li Lucy, Suchin Gururangan, Luca Soldaini, et al.*2024 · 15 citations*
250. [**Towards Ontology Construction with Language Models arXiv:2309.09898**Maurice Funk, Simon Hosemann, Jean Christoph Jung, et al.*2023 · 12 citations*](https://md.research.elamur.ai/free/en/papers/funk-2023-towards-ontology-construction-language-models/)
251. [**TL;DR: Too Long, Do Re-weighting for Efficient LLM Reasoning Compression arXiv:2506.02678**Zhong-Zhi Li, Xiao Liang, Zihao Tang, et al.*2025 · 8 citations*](https://md.research.elamur.ai/free/en/papers/li-2025-tl-dr-too-long-re/)
252. **[Towards Dynamic Theory of Mind: Evaluating LLM Adaptation to Temporal Evolution of Human States](https://md.research.elamur.ai/free/en/papers/xiao-2025-dynamic-theory-mind-evaluating-llm/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yang Xiao, Jiashuo Wang, Qiancheng Xu, et al.*2025 · 8 citations*
253. [**Scaling Smart: Accelerating Large Language Model Pre-training with Small Model Initialization arXiv:2409.12903**Mohammad Samragh, Iman Mirzadeh, Keivan Alizadeh Vahid, et al.*2024 · 8 citations*](https://md.research.elamur.ai/free/en/papers/samragh-2024-scaling-smart-accelerating-large-language/)
254. **[MetaScale: Test-Time Scaling with Evolving Meta-Thoughts](https://md.research.elamur.ai/free/en/papers/liu-2026-metascale-test-time-scaling-evolving/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Qin Liu, Wenxuan Zhou, Nan Xu, et al.*2026 · 7 citations*
255. **[How Good is Your Wikipedia? Auditing Data Quality for Low-resource and Multilingual NLP](https://md.research.elamur.ai/free/en/papers/tatariya-2026-how-good-is-your-wikipedia/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kushal Tatariya, Artur Kulmizev, Wessel Poelman, et al.*2026 · 6 citations*
256. **[FineReason: Evaluating and Improving LLMs' Deliberate Reasoning through Reflective Puzzle Solving](https://md.research.elamur.ai/free/en/papers/chen-2025-finereason-evaluating-improving-llms-deliberate/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Guizhen Chen, Weiwen Xu, Hao Zhang, et al.*2025 · 6 citations*
257. [**Arabic Stable LM: Adapting Stable LM 2 1.6B to Arabic arXiv:2412.04277**Zaid Alyafeai, Michael Pieler, Hannah Teufel, et al.*2024 · 6 citations*](https://md.research.elamur.ai/free/en/papers/alyafeai-2024-arabic-stable-lm-adapting-stable/)
258. **[VisAidMath: Benchmarking Visual-Aided Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/ma-2026-visaidmath-benchmarking-visual-aided-mathematical/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jingkun Ma, Runzhe Zhan, Yang Li, et al.*2026 · 5 citations*
259. [**WanJuan-CC: A Safe and High-Quality Open-sourced English Webtext Dataset arXiv:2402.19282**Jiantao Qiu, Haijun Lv, Zhenjiang Jin, et al.*2024 · 5 citations*](https://md.research.elamur.ai/free/en/papers/qiu-2024-wanjuan-cc-safe-high-quality/)
260. **[Aleph-Alpha-GermanWeb: Improving German-language LLM pre-training with model-based data curation and synthetic data generation](https://md.research.elamur.ai/free/en/papers/burns-2026-aleph-alpha-germanweb-improving-german/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Thomas F Burns, Letitia Parcalabescu, Stephan Waeldchen, et al.*2026 · 4 citations*
261. **[Barriers to Discrete Reasoning with Transformers: A Survey Across Depth, Exactness, and Bandwidth](https://md.research.elamur.ai/free/en/papers/yuan-2026-barriers-discrete-reasoning-transformers-survey/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Michelle Yuan, Weiyi Sun, Amir H. Rezaeian, et al.*2026 · 3 citations*
262. [**The German Commons - 154 Billion Tokens of Openly Licensed Text for German Language Models arXiv:2510.13996**Lukas Gienapp, Christopher Schroder, Stefan Schweter, et al.*2025 · 2 citations*](https://md.research.elamur.ai/free/en/papers/gienapp-2025-german-commons-154-billion-tokens/)
263. [**Uncovering More Shallow Heuristics: Probing the Natural Language Inference Capacities of Transformer-Based Pre-Trained Language Models Using Syllogistic Patterns arXiv:2201.07614**Reto Gubelmann, Siegfried Handschuh*2022 · 2 citations*](https://md.research.elamur.ai/free/en/papers/gubelmann-2022-uncovering-more-shallow-heuristics-probing/)
264. **[AMALIA: A Fully Open Large Language Model for European Portuguese](https://md.research.elamur.ai/free/en/papers/simplicio-2026-amalia-fully-open-large-language/) [International Conference on Computational Processing of Portuguese](https://md.research.elamur.ai/free/en/venues/propor/)**Afonso Simplício, Gonçalo Vinagre, Miguel Moura Ramos, et al.*2026 · 1 citation*
265. **[From Curated Data to Scalable Models: Continual Pre-training of Dense and MoE Large Language Models for Tibetan](https://md.research.elamur.ai/free/en/papers/yang-2026-curated-data-scalable-models-continual/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lei Yang, Leiyu Pan, Bojian Xiong, et al.*2026 · 1 citation*
266. **[SepSeq: A Training-Free Framework for Long Numerical Sequence Processing in LLMs](https://md.research.elamur.ai/free/en/papers/sun-2026-sepseq-training-free-framework-long/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jie Sun, Yu Liu, Lu Han, et al.*2026 · 1 citation*
267. **[sutta at SemEval-2026 Task 12: A Multi-Perspective Retrieve-Verify-Aggregate Framework for Abductive Event Reasoning](https://md.research.elamur.ai/free/en/papers/zou-2026-sutta-semeval-2026-task-12/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Junliu Zou, Liang Yang, Jingjie Zeng*2026 · 1 citation*
268. **[The Thin Line Between Comprehension and Persuasion in LLMs](https://md.research.elamur.ai/free/en/papers/wynter-2026-thin-line-between-comprehension-persuasion/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Adrian de Wynter, Tangming Yuan*2026 · 1 citation*
269. **[ArabicWeb-Edu: Educational Quality Data for Arabic LLM Training](https://md.research.elamur.ai/free/en/papers/hawasly-2025-arabicweb-edu-educational-quality-data/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Majd Hawasly, Tasnim Mohiuddin, Hamdy Mubarak, et al.*2025 · 1 citation*
270. [**Mix, MinHash, and Match: Cross-Source Agreement for Multilingual Pretraining Datasets arXiv:2512.18834**Sultan Alrashed, Francesco Orabona*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/alrashed-2025-mix-minhash-match-cross-source/)
271. **[A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions](https://md.research.elamur.ai/free/en/papers/yu-2026-survey-reinforcement-learning-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhiyin Yu, Yuchen Mou, Juncheng Yan, et al.*2026*
272. **[Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory and Test-Time Compute Scaling](https://md.research.elamur.ai/free/en/papers/rodkin-2026-beyond-memorization-extending-reasoning-depth/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ivan Rodkin, Daniil Orel, Konstantin Smirnov, et al.*2026*
273. **[Challenging the Myth: A Research Arc on LLMs as Human Simulacra](https://md.research.elamur.ai/free/en/papers/munker-2026-challenging-myth-research-arc-llms/) [Big Picture v2: Crafting a Research Narrative](https://md.research.elamur.ai/free/en/venues/bigpicture/)**Simon Münker, Achim Rettinger, Damian Trilling*2026*
274. **[DualFact+: A Multimodal Fact Verification Framework for Procedural Video Captioning](https://md.research.elamur.ai/free/en/papers/oguz-2026-dualfact-multimodal-fact-verification-framework/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Cennet Oguz, Yasser Hamidullah, Josef van Genabith, et al.*2026*
275. [**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/)
276. **[How to Train a Real-World Silicon Concierge? Internalizing Complex Business Workflow to Only OneModel](https://md.research.elamur.ai/free/en/papers/tong-2026-how-train-real-world-silicon/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yongqi Tong, Xiaoyun Feng, Lyuxin Xue, et al.*2026*
277. **[HSS-Synth: Humanities and Social Sciences Data Synthesis for LLMs](https://md.research.elamur.ai/free/en/papers/peng-2026-hss-synth-humanities-social-sciences/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ru Peng, Tianyu Zhao, Xijun Gu, et al.*2026*
278. **[Language Acquisition Device in Large Language Models](https://md.research.elamur.ai/free/en/papers/mita-2026-language-acquisition-device-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Masato Mita, Taiga Someya, Ryo Yoshida, et al.*2026*
279. **[Losing our Tail, Again: (Un)Natural Selection & Multilingual LLMs](https://md.research.elamur.ai/free/en/papers/vanmassenhove-2026-losing-our-tail-again-un/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Eva Vanmassenhove*2026*
280. **[MaskTab: Scalable Masked Tabular Pretraining with Scaling Laws and Distillation for Industrial Classification](https://md.research.elamur.ai/free/en/papers/zheng-2026-masktab-scalable-masked-tabular-pretraining/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Bo Zheng, Yudong Chen, Zihua Xiong, et al.*2026*
281. **[Measuring Idiomaticity in Text Embedding Models with epsilon-compositionality](https://md.research.elamur.ai/free/en/papers/wold-2026-measuring-idiomaticity-text-embedding-models/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Sondre Wold, Étienne Simon, Erik Velldal, et al.*2026*
282. **[Merge and Conquer: Instructing Multilingual Models by Adding Target Language Weights](https://md.research.elamur.ai/free/en/papers/valero-2026-merge-conquer-instructing-multilingual-models/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Eneko Valero, Maria Ribalta i Albado, Oscar Sainz, et al.*2026*
283. **[Multi-Constraint State Tracking with Negation: A Diagnostic Benchmark for LLM World Modeling](https://md.research.elamur.ai/free/en/papers/sar-2026-multi-constraint-state-tracking-negation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ayan Sar, Pranav Singh Puri, Sumit Aich, et al.*2026*
284. **[MUTANT: A Recipe for Multilingual Tokenizer Design](https://md.research.elamur.ai/free/en/papers/rana-2026-mutant-recipe-multilingual-tokenizer-design/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Souvik Rana, Ashish Kulkarni, Arul Menezes, et al.*2026*
285. **[MzansiText and MzansiLM: An Open Corpus and Decoder-Only Language Model for South African Languages](https://md.research.elamur.ai/free/en/papers/lombard-2026-mzansitext-mzansilm-open-corpus-decoder/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Anri M. Lombard, Simbarashe Mawere, Temi Aina, et al.*2026*
286. **[Orthographic Constraint Satisfaction and Human Difficulty Alignment in Large Language Models](https://md.research.elamur.ai/free/en/papers/tuck-2026-orthographic-constraint-satisfaction-human-difficulty/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Bryan E. Tuck, Rakesh Verma*2026*
287. **[PILOT: Planning via Internalized Latent Optimization Trajectories for Large Language Models](https://md.research.elamur.ai/free/en/papers/zheng-2026-pilot-planning-internalized-latent-optimization/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haoyu Zheng, Yun Zhu, Yuqian Yuan, et al.*2026*
288. **[PRISM: Probing Reasoning, Instruction, and Source Memory in LLM Hallucinations](https://md.research.elamur.ai/free/en/papers/wu-2026-prism-probing-reasoning-instruction-source/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuhe Wu, Guangyu Wang, Yuran Chen, et al.*2026*
289. **[Program-of-Thought Reveals LLM Abstraction Ceilings](https://md.research.elamur.ai/free/en/papers/zhou-2026-program-thought-reveals-llm-abstraction/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mike Zhou, Fenil Bardoliya, Vivek Gupta, et al.*2026*
290. **[RELIC: Evaluating Complex Reasoning via the Recognition of Languages In-Context](https://md.research.elamur.ai/free/en/papers/petty-2026-relic-evaluating-complex-reasoning-recognition/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Jackson Petty, Michael Y. Hu, Wentao Wang, et al.*2026*
291. [**Repetition over Diversity: High-Signal Data Filtering for Sample-Efficient German Language Modeling arXiv:2604.28075**Ansar Aynetdinov, Patrick Haller, Alan Akbik*2026*](https://md.research.elamur.ai/free/en/papers/aynetdinov-2026-repetition-over-diversity-high-signal/)
292. **[SELECting over Tokens: Curating Pre-training Data at Scale via Token Classification](https://md.research.elamur.ai/free/en/papers/tong-2026-selecting-over-tokens-curating-pre/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xin Tong, Weidong Zhang, Jiaang Li, et al.*2026*
293. **[The Dog the Cat Chased Stumped the Model: Measuring When Language Models Abandon Structure for Shortcuts](https://md.research.elamur.ai/free/en/papers/madhusudan-2026-dog-cat-chased-stumped-model/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Sangmitra Madhusudan, Kaige Chen, Ali Emami*2026*
294. **[The Imperfective Paradox in Large Language Models](https://md.research.elamur.ai/free/en/papers/ma-2026-imperfective-paradox-large-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Bolei Ma, Yusuke Miyao*2026*
295. [**The Validity of Coreference-based Evaluations of Natural Language Understanding arXiv:2602.16200**Ian Porada*2026*](https://md.research.elamur.ai/free/en/papers/porada-2026-validity-coreference-based-evaluations-natural/)
296. **[To Skip, to Swap or to Not Swap? Identifying Step Transition Types in Instructional Manuals](https://md.research.elamur.ai/free/en/papers/yang-2026-skip-swap-or-not-swap/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Hsiu-Yu Yang, Michael Roth, Andreas Bulling, et al.*2026*
297. **[Toward Consistent World Models with Multi-Token Prediction and Latent Semantic Enhancement](https://md.research.elamur.ai/free/en/papers/zhong-2026-consistent-world-models-multi-token/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qimin Zhong, Hao Liao, Haiming Qin, et al.*2026*
298. **[TrustTable: A Neuro-Symbolic Auditing Framework for Faithful Table QA](https://md.research.elamur.ai/free/en/papers/zhao-2026-trusttable-neuro-symbolic-auditing-framework/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Guangzhen Zhao, Dechang Kong, Tongyu Wu, et al.*2026*
299. **[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)](https://md.research.elamur.ai/free/en/papers/gao-2025-key-technologies-large/) [Journal of Software](https://md.research.elamur.ai/free/en/venues/journal-of-software/)**Yanjie Gao (高彦杰), Yueguo Chen (陈跃国)*2025*
300. [**Wasm: A Pipeline for Constructing Structured Arabic Interleaved Multimodal Corpora arXiv:2511.07080**Khalil Hennara; Ahmad Bastati; Muhammad Hreden; Mohamed Motasim Hamed; Zeina Aldallal; Sara Chrouf;, Safwan AlModhayan*2025*](https://md.research.elamur.ai/free/en/papers/hennara-2025-wasm-pipeline-constructing-structured-arabic/)

[← prev](https://md.research.elamur.ai/free/en/authors/xiang-lorraine-li/cited-by/page/2/) [1](https://md.research.elamur.ai/free/en/authors/xiang-lorraine-li/cited-by/) [2](https://md.research.elamur.ai/free/en/authors/xiang-lorraine-li/cited-by/page/2/) **3** [4](https://md.research.elamur.ai/free/en/authors/xiang-lorraine-li/cited-by/page/4/) [next →](https://md.research.elamur.ai/free/en/authors/xiang-lorraine-li/cited-by/page/4/)

- [← back to](https://md.research.elamur.ai/free/en/authors/xiang-lorraine-li/) — Xiang Lorraine Li