---
title: "Who cites Eric Hambro — page 4"
description: "5,793 papers of the Elamur Research library cite the works of Eric Hambro, found in their bibliographies · page 4 of 4 · 301–382; the most cited first."
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---
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# Who cites Eric Hambro

5,824 citations of their works from 5,793 papers of our library (of the 140,884 whose bibliographies we read) · 382 with a page here, the most cited first · page 4 of 4 · 301–382 · [← Eric Hambro](https://md.research.elamur.ai/free/en/authors/eric-hambro/)

[← prev](https://md.research.elamur.ai/free/en/authors/eric-hambro/cited-by/page/3/) [1](https://md.research.elamur.ai/free/en/authors/eric-hambro/cited-by/) [2](https://md.research.elamur.ai/free/en/authors/eric-hambro/cited-by/page/2/) [3](https://md.research.elamur.ai/free/en/authors/eric-hambro/cited-by/page/3/) **4**

301. [**Towards Effective Disambiguation for Machine Translation with Large Language Models**Vivek Iyer, Pinzhen Chen, Alexandra Birch*2023 · 38 citations*](https://md.research.elamur.ai/free/en/papers/iyer-2023-effective-disambiguation-machine-translation-large/)
302. [**Towards Cross-Tokenizer Distillation: the Universal Logit Distillation Loss for LLMs**Nicolas Boizard, Kevin El Haddad, Celine Hudelot, et al.*2024 · 34 citations*](https://md.research.elamur.ai/free/en/papers/boizard-2024-towards-cross-tokenizer-distillation-universal/)
303. [**Observational Scaling Laws and the Predictability of Language Model Performance**Yangjun Ruan, Chris J. Maddison, Tatsunori Hashimoto*2024 · 28 citations*](https://md.research.elamur.ai/free/en/papers/ruan-2024-observational-scaling-laws-predictability-language/)
304. [**MusiLingo: Bridging Music and Text with Pre-trained Language Models for Music Captioning and Query Response**Zihao Deng, Yinghao Ma, Yudong Liu, et al.*2024 · 27 citations*](https://md.research.elamur.ai/free/en/papers/deng-2024-musilingo-bridging-music-text-pre/)
305. [**Exploring the Relationship between LLM Hallucinations and Prompt Linguistic Nuances: Readability, Formality, and Concreteness**Vipula Rawte, Prachi Priya, S.M Towhidul Islam Tonmoy, et al.*2023 · 27 citations*](https://md.research.elamur.ai/free/en/papers/rawte-2023-exploring-relationship-between-llm-hallucinations/)
306. [**Prediction of Arabic Legal Rulings using Large Language Models**Adel Ammar, Anis Koubaa, Bilel Benjdira, et al.*2023 · 27 citations*](https://md.research.elamur.ai/free/en/papers/ammar-2023-prediction-arabic-legal-rulings-using/)
307. [**Are Large Language Models Robust Coreference Resolvers?**Nghia T. Le, Alan Ritter*2023 · 26 citations*](https://md.research.elamur.ai/free/en/papers/le-2023-large-language-models-robust-coreference/)
308. [**Taqyim: Evaluating Arabic NLP Tasks Using ChatGPT Models**Zaid Alyafeai, Maged S. Alshaibani, Badr AlKhamissi, et al.*2023 · 25 citations*](https://md.research.elamur.ai/free/en/papers/alyafeai-2023-taqyim-evaluating-arabic-nlp-tasks/)
309. [**Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations**Alexander Hägele, Elie Bakouch, Atli Kosson, et al.*2024 · 24 citations*](https://md.research.elamur.ai/free/en/papers/hagele-2024-scaling-laws-compute-optimal-training/)
310. [**Distillation Scaling Laws**Dan Busbridge, Amitis Shidani, Floris Weers, et al.*2025 · 23 citations*](https://md.research.elamur.ai/free/en/papers/busbridge-2025-distillation-scaling-laws/)
311. [**Developing a Pragmatic Benchmark for Assessing Korean Legal Language Understanding in Large Language Models**Yeeun Kim, Youngrok Choi, Eunkyung Choi, et al.*2024 · 22 citations*](https://md.research.elamur.ai/free/en/papers/kim-2024-developing-pragmatic-benchmark-assessing-korean/)
312. [**CofiPara: A Coarse-to-fine Paradigm for Multimodal Sarcasm Target Identification with Large Multimodal Models**Zixin Chen, Hongzhan Lin, Ziyang Luo, et al.*2024 · 20 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-cofipara-coarse-fine-paradigm-multimodal/)
313. [**Synergetic Event Understanding: A Collaborative Approach to Cross-Document Event Coreference Resolution with Large Language Models**Qingkai Min, Qipeng Guo, Xiangkun Hu, et al.*2024 · 19 citations*](https://md.research.elamur.ai/free/en/papers/min-2024-synergetic-event-understanding-collaborative-approach/)
314. [**A Rationale-centric Counterfactual Data Augmentation Method for Cross-Document Event Coreference Resolution**Bowen Ding, Qingkai Min, Shengkun Ma, et al.*2024 · 17 citations*](https://md.research.elamur.ai/free/en/papers/ding-2024-rationale-centric-counterfactual-data-augmentation/)
315. [**New Trends for Modern Machine Translation with Large Reasoning Models**Sinuo Liu, Chenyang Lyu, Minghao Wu, et al.*2026 · 16 citations*](https://md.research.elamur.ai/free/en/papers/liu-2026-new-trends-modern-machine-translation/)
316. [**Does DetectGPT Fully Utilize Perturbation? Bridging Selective Perturbation to Fine-tuned Contrastive Learning Detector would be Better**Shengchao Liu, Xiaoming Liu, Yichen Wang, et al.*2024 · 16 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-does-detectgpt-fully-utilize-perturbation/)
317. [**Adapting LLMs to Hebrew: Unveiling DictaLM 2.0 with Enhanced Vocabulary and Instruction Capabilities**Shaltiel Shmidman, Avi Shmidman, Amir DN Cohen, et al.*2024 · 15 citations*](https://md.research.elamur.ai/free/en/papers/shmidman-2024-adapting-llms-hebrew-unveiling-dictalm/)
318. [**Resolving Discrepancies in Compute-Optimal Scaling of Language Models**Tomer Porian, Mitchell Wortsman, Jenia Jitsev, et al.*2024 · 14 citations*](https://md.research.elamur.ai/free/en/papers/porian-2024-resolving-discrepancies-compute-optimal-scaling/)
319. [**Constituency Parsing using LLMs**Xuefeng Bai, Jialong Wu, Yulong Chen, et al.*2023 · 14 citations*](https://md.research.elamur.ai/free/en/papers/bai-2023-constituency-parsing-using-llms/)
320. [**TigerCoder: A Novel Suite of LLMs for Code Generation in Bangla**Nishat Raihan, Antonios Anastasopoulos, Marcos Zampieri*2026 · 13 citations*](https://md.research.elamur.ai/free/en/papers/raihan-2026-tigercoder-novel-suite-llms-code/)
321. [**Bridging Background Knowledge Gaps in Translation with Automatic Explicitation**HyoJung Han, Jordan Boyd-Graber, Marine Carpuat*2023 · 13 citations*](https://md.research.elamur.ai/free/en/papers/han-2023-bridging-background-knowledge-gaps-translation/)
322. [**DiscoFlan: Instruction Fine-tuning and Refined Text Generation for Discourse Relation Label Classification**Kaveri Anuranjana*2023 · 13 citations*](https://md.research.elamur.ai/free/en/papers/anuranjana-2023-discoflan-instruction-fine-tuning-refined/)
323. [**Can Large Language Models Address Open-Target Stance Detection?**Abu Ubaida Akash, Ahmed Fahmy, Amine Trabelsi*2025 · 12 citations*](https://md.research.elamur.ai/free/en/papers/akash-2025-can-large-language-models-address/)
324. [**DiBiMT: A Gold Evaluation Benchmark for Studying Lexical Ambiguity in Machine Translation**Federico Martelli, Stefano Perrella, Niccolò Campolungo, et al.*2025 · 11 citations*](https://md.research.elamur.ai/free/en/papers/martelli-2025-dibimt-gold-evaluation-benchmark-studying/)
325. [**How well can LLMs Grade Essays in Arabic?**Rayed Ghazawi, Edwin Simpson*2025 · 11 citations*](https://md.research.elamur.ai/free/en/papers/ghazawi-2025-well-llms-grade-essays-arabic/)
326. [**Pragmatic Competence Evaluation of Large Language Models for the Korean Language**Dojun Park, Jiwoo Lee, Hyeyun Jeong, et al.*2024 · 11 citations*](https://md.research.elamur.ai/free/en/papers/park-2024-pragmatic-competence-evaluation-large-language/)
327. [**Overview of PragTag-2023: Low-Resource Multi-Domain Pragmatic Tagging of Peer Reviews**Nils Dycke, Ilia Kuznetsov, Iryna Gurevych*2023 · 10 citations*](https://md.research.elamur.ai/free/en/papers/dycke-2023-overview-pragtag-2023-low-resource/)
328. [**The Arabic AI Fingerprint: Stylometric Analysis and Detection of Large Language Models Text**Maged S. Al-Shaibani, Moataz Ahmed*2025 · 7 citations*](https://md.research.elamur.ai/free/en/papers/al-shaibani-2025-arabic-ai-fingerprint-stylometric-analysis/)
329. [**Cutting Through the Noise: Boosting LLM Performance on Math Word Problems**Ujjwala Anantheswaran, Himanshu Gupta, Kevin Scaria, et al.*2024 · 7 citations*](https://md.research.elamur.ai/free/en/papers/anantheswaran-2024-cutting-through-noise-boosting-llm/)
330. [**ALADAN at IWSLT24 Low-resource Arabic Dialectal Speech Translation Task**Waad Ben Kheder, Josef Jon, André Beyer, et al.*2024 · 4 citations*](https://md.research.elamur.ai/free/en/papers/kheder-2024-aladan-iwslt24-low-resource-arabic/)
331. [**GemmAr: Enhancing LLMs Through Arabic Instruction-Tuning**Hasna Chouikhi, Manel Aloui, Cyrine Ben Hammou, et al.*2024 · 4 citations*](https://md.research.elamur.ai/free/en/papers/chouikhi-2024-gemmar-enhancing-llms-through-arabic/)
332. [**A Multi-Task Benchmark for Abusive Language Detection in Low-Resource Settings**Fitsum Gaim, Hoyun Song, Huije Lee, et al.*2025 · 3 citations*](https://md.research.elamur.ai/free/en/papers/gaim-2025-multi-task-benchmark-abusive-language/)
333. [**Evaluating large language models for the tasks of PoS tagging within the Universal Dependency framework**Mateus Machado, Evandro Ruiz*2024 · 3 citations*](https://md.research.elamur.ai/free/en/papers/machado-2024-evaluating-large-language-models-tasks/)
334. [**Subject-verb agreement with Seq2Seq transformers: Bigger is better, but still not best**Michael Wilson, Zhenghao Zhou, Robert Frank*2023 · 3 citations*](https://md.research.elamur.ai/free/en/papers/wilson-2023-subject-verb-agreement-seq2seq-transformers/)
335. [**Addressing the Ecological Fallacy in Larger LMs with Human Context**Nikita Soni, Dhruv Vijay Kunjadiya, Pratham Piyush Shah, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/soni-2026-addressing-ecological-fallacy-larger-lms/)
336. [**ArabicDialectSafety: A Dialect-Aware Benchmark for Arabic Content Safety Classification**Wajdi Zaghouani, Md. Rafiul Biswas, Kholoud Khalil Aldous, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/zaghouani-2026-arabicdialectsafety-dialect-aware-benchmark-arabic/)
337. [**Conversational Assistants to Support Patients with Heart Failure: Comparing a Neurosymbolic Architecture with GPT**Anuja Tayal, Devika Salunke, Barbara Di Eugenio, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/tayal-2026-conversational-assistants-support-patients-heart/)
338. [**CorefInst: Leveraging LLMs for Multilingual Coreference Resolution**Tuğba Pamay Arslan, Emircan Erol, Gülşen Eryiğit*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/arslan-2026-corefinst-leveraging-llms-multilingual-coreference/)
339. [**Do LLMs Judge Distantly Supervised Named Entity Labels Well? Constructing the JudgeWEL Dataset**Alistair Plum, Laura Maria Bernardy, Tharindu Ranasinghe*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/plum-2026-do-llms-judge-distantly-supervised/)
340. [**HPLT 3.0: Very Large-Scale Multilingual Resources for LLMs and MT. Mono- and Bi-lingual Data, Multilingual Evaluation, and Pre-Trained Models**Stephan Oepen, Nikolay Arefyev, Mikko Aulamo, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/oepen-2026-hplt-3-0-very-large/)
341. [**What Exactly do Children Receive in Language Acquisition? A Case Study on CHILDES with Automated Detection of Filler-Gap Dependencies**Zhenghao Zhou, William Dai, Maya Viswanathan, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/zhou-2026-what-exactly-do-children-receive/)
342. [**Babies Learn to Look Ahead: Multi-Token Prediction in Small LMs**Ansar Aynetdinov, Alan Akbik*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/aynetdinov-2025-babies-learn-look-ahead-multi/)
343. [**Bridging the Socioeconomic Gap in Education: A Hybrid AI and Human Annotation Approach**Nahed Abdelgaber, Labiba Jahan, Arham Vinit Doshi, et al.*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/abdelgaber-2025-bridging-socioeconomic-gap-education-hybrid/)
344. [**Beyond Error Categories: A Contextual Approach of Evaluating Emerging Spell and Grammar Checkers**Þórunn Arnardóttir, Svanhvít Lilja Ingólfsdóttir, Haukur Barri Símonarson, et al.*2024 · 1 citation*](https://md.research.elamur.ai/free/en/papers/arnardottir-2024-beyond-error-categories-contextual-approach/)
345. [**A Cascade Reinforcement Learning Strategy for Efficiently Enhancing the Reasoning Ability of Multimodal Large Language Models (高效提升多模态大语言模型推理能力的级联强化学习策略)**Weiyun Wang (王玮赟), Hengjun Pu (蒲恒骏), Linglin Jing (景凌林), et al.*2026*](https://md.research.elamur.ai/free/en/papers/wang-2026-cascade-reinforcement-learning/)
346. [**A Novel Synthetic Dataset for Few-Shot Legal Relation Extraction in German**Shiva Banasaz Nouri, Elena Leitner, Julian Moreno-Schneider, et al.*2026*](https://md.research.elamur.ai/free/en/papers/nouri-2026-novel-synthetic-dataset-few-shot/)
347. [**A Survey on Parameter-efficient Fine-tuning of Large Models: Techniques, Trends, and Challenges (大模型参数高效微调方法综述: 技术、趋势与挑战)**Anda Tang (唐岸达), Zhouchen Lin (林宙辰)*2026*](https://md.research.elamur.ai/free/en/papers/tang-2026-parameter-efficient-fine/)
348. [**AYN: A Tiny Yet Competitive Indian Legal Language Model Pretrained from Scratch**Mitodru Niyogi, Eric Gaussier, Arnab Bhattacharya*2026*](https://md.research.elamur.ai/free/en/papers/niyogi-2026-ayn-tiny-yet-competitive-indian/)
349. [**Beyond Poetry: Can Large Language Models Generate Classical Arabic Maqamat?**AbdulRahman A. Morsy, Aya Zirikly Department of Computer Science, School of Engineering, et al.*2026*](https://md.research.elamur.ai/free/en/papers/morsy-2026-poetry-large-language-models-generate/)
350. [**Consistency of LLMs to Comparative Statements in Mathematical Reasoning Tasks**Aidan W. San, Daniel Juyoung Son, Xiaodong Liu, et al.*2026*](https://md.research.elamur.ai/free/en/papers/san-2026-consistency-llms-comparative-statements-mathematical/)
351. [**Corpus and Baselines for Distinguishing Authentic, AI-Generated, and AI-Enhanced Resumes**Andrea Loizidou, Anshu Kiran Sharma, Adrian Esquivel, et al.*2026*](https://md.research.elamur.ai/free/en/papers/loizidou-2026-corpus-baselines-distinguishing-authentic-ai/)
352. [**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/)
353. [**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/)
354. [**From Graph to Text and Back: Semantic Fidelity in Automated Industrial Knowledge Graphs**Kamyar Zeinalipour, Silvia Severini, Alessia Borghini, et al.*2026*](https://md.research.elamur.ai/free/en/papers/zeinalipour-2026-graph-text-back-semantic-fidelity/)
355. [**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/)
356. [**GhostWriter: Hidden AI-Generated Texts over Multiple Languages, Domains and Generators**Manuel Schaaf, Kevin Bönisch, Alexander Mehler*2026*](https://md.research.elamur.ai/free/en/papers/schaaf-2026-ghostwriter-hidden-ai-generated-texts/)
357. [**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/)
358. [**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/)
359. [**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/)
360. [**Mimicking Neural Machine Translation History for Pedagogic Reasons**Vincent Vandeghinste*2026*](https://md.research.elamur.ai/free/en/papers/vandeghinste-2026-mimicking-neural-machine-translation-history/)
361. [**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/)
362. [**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/)
363. [**Modular Neural Machine Translation with a Semantic Pivot - Pilot Study Using AMR**Wenyang Gao, Yaxuan Li, Yunxin Bao, et al.*2026*](https://md.research.elamur.ai/free/en/papers/gao-2026-modular-neural-machine-translation-semantic/)
364. [**Multi-Agent Debate for Machine Translation: A Case Study on English-Japanese Translation**Zhan Shen, Jason Naradowsky, Xiaotian Wang, et al.*2026*](https://md.research.elamur.ai/free/en/papers/shen-2026-multi-agent-debate-machine-translation/)
365. [**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/)
366. [**Obscuring Data Contamination Through Translation: Evidence from Arabic Corpora**Chaymaa Abbas, Nour Shamaa, Mariette Awad*2026*](https://md.research.elamur.ai/free/en/papers/abbas-2026-obscuring-data-contamination-through-translation/)
367. [**One Size Does Not Fit All: Why EU Legislative Translation Demands Domain-Specific Fine-Tuning of LLMs**Valerio Lorini, Paula Vlaic, Ulascan Akbulut, et al.*2026*](https://md.research.elamur.ai/free/en/papers/lorini-2026-one-size-does-not-fit/)
368. [**PrePPER: A Preference Pattern-based Profiling Framework for Explainable Recommendation**Taisuke Usumi, Akiko Masaki, Sanae Muramatsu, et al.*2026*](https://md.research.elamur.ai/free/en/papers/usumi-2026-prepper-preference-pattern-based-profiling/)
369. [**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/)
370. [**Rethinking Gender Annotation for Bias Evaluation in Machine Translation: Can LLMs Improve Reliability?**Chiara Manna, Argentina Anna Rescigno, Eva Vanmassenhove*2026*](https://md.research.elamur.ai/free/en/papers/manna-2026-rethinking-gender-annotation-bias-evaluation/)
371. [**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/)
372. [**Terminology-Aware Retrieval-Augmented Knowledge Distillation for Biomedical Neural Machine Translation**Maria Zafar, Souhail Bakkali, Rejwanul Haque*2026*](https://md.research.elamur.ai/free/en/papers/zafar-2026-terminology-aware-retrieval-augmented-knowledge/)
373. [**Translation-CoT: A Human-Inspired Chain-of-Thought Framework for Multilingual LLM Translation**Tabia Tanzin Prama, Juniper L Lovato, Chris Danforth, et al.*2026*](https://md.research.elamur.ai/free/en/papers/prama-2026-translation-cot-human-inspired-chain/)
374. [**Bridging Laughter Across Languages: Generation of Hindi-English Code-mixed Puns**Likhith Asapu, Prashant Kodali, Ashna Dua, et al.*2025*](https://md.research.elamur.ai/free/en/papers/asapu-2025-bridging-laughter-across-languages-generation/)
375. [**Embodied Intelligence Systems Based on Large Models: A Survey (基于大模型的具身智能系统综述)**Wensheng Wang (王文晟), Ning Tan (谭宁), Kai Huang (黄凯), et al.*2025*](https://md.research.elamur.ai/free/en/papers/wang-2025-embodied-intelligence-systems/)
376. [**MuDRiC: Multi-Dialect Reasoning for Arabic Commonsense Validation**Kareem Elozeiri, Mervat Abassy, Preslav Nakov, et al.*2025*](https://md.research.elamur.ai/free/en/papers/elozeiri-2025-mudric-multi-dialect-reasoning-arabic/)
377. [**NeoDictaBERT: Pushing the Frontier of BERT models for Hebrew**Shaltiel Shmidman, Avi Shmidman, Moshe Koppel*2025*](https://md.research.elamur.ai/free/en/papers/shmidman-2025-neodictabert-pushing-frontier-bert-models/)
378. [**Survey on Code Generation with LLM-based Agents (基于大语言模型智能体的代码生成综述)**Yihong Dong (董益宏), Xue Jiang (姜雪), Jiaru Qian (钱家如), et al.*2025*](https://md.research.elamur.ai/free/en/papers/dong-2025-code-generation-llm/)
379. [**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/)
380. [**From Foundation Intelligence to General Intelligence: Status and Prospects of Large-Model-Based GenAI and AGI (从基础智能到通用智能: 基于大模型的GenAI和AGI之现状与展望)**Qinghai Miao (缪青海), Xingxia Wang (王兴霞), Jing Yang (杨静), et al.*2024*](https://md.research.elamur.ai/free/en/papers/miao-2024-foundation-intelligence-general/)
381. [**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/)
382. [**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/)

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