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Who cites Lukas Blecher

7,156 citations of their works from 7,145 papers of our library (of the 149,439 whose bibliographies we read) · 1,417 with a page here, the most cited first · page 15 of 15 · 1401–1417 · ← Lukas Blecher

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  1. Who Generates More Empathetic Responses–-Humans or LLMs? A Comparative Evaluation with Human and LLM JudgesConference on Computational Natural Language Learning (CoNLL)Anuradha Welivita, Fawzia Zeitoun, Pearl Pu2026
  2. XMark: Reliable Multi-Bit Watermarking for LLM-Generated TextsAnnual Meeting of the Association for Computational Linguistics (ACL)Jiahao Xu, Rui Hu, Olivera Kotevska, et al.2026
  3. You Only Need One Single Token to Refine Safety AlignmentFindings of the Association for Computational LinguisticsWenqian Yu, Shuo Chen, Zhijiang Li, et al.2026
  4. ZIP: Quantifying Which Words Matter in Zero-Shot Instructional PromptsJoint Conference on Lexical and Computational Semantics (*SEM)Nikta Gohari Sadr, Sangmitra Madhusudan, Arash Asgari, et al.2026
  5. A MISMATCHED Benchmark for Scientific Natural Language InferenceFindings of the Association for Computational LinguisticsFiroz Shaik, Mobashir Sadat, Nikita Gautam, et al.2025
  6. Aligning Language Models for Icelandic Legal Text SummarizationNordic Conference on Computational Linguistics (NoDaLiDa)Þórir Hrafn Harðarson, Hrafn Loftsson, Stefán Ólafsson2025
  7. Are Lexicon-Based Tools Still the Gold Standard for Valence Analysis in Low-Resource Flemish?arXiv:2506.04139 · Presented at LLM Evaluation Workshop, NeurIPS 2025Ratna Kandala, Katie Hoemann2025
  8. Embodied Intelligence Systems Based on Large Models: A Survey (基于大模型的具身智能系统综述)Acta Automatica Sinica (自动化学报)Wensheng Wang (王文晟), Ning Tan (谭宁), Kai Huang (黄凯), et al.2025
  9. Math Natural Language Inference: this should be easy!Joint Conference on Lexical and Computational Semantics (*SEM)Valeria de Paiva, Qiyue Gao, Hai Hu, et al.2025
  10. 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)Journal of SoftwareYihong Dong (董益宏), Xue Jiang (姜雪), Jiaru Qian (钱家如), et al.2025
  11. 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)Journal of SoftwareYanjie Gao (高彦杰), Yueguo Chen (陈跃国)2025
  12. 360Zhinao Technical ReportarXiv:2405.133862024
  13. 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)Annual Meeting of the Association for Natural Language Processing麻場直喜, 野崎雄太, 中島大, et al.2024
  14. Can generative AI recognise textual entailment? (生成 AI は含意関係認識ができるのか)Annual Meeting of the Association for Natural Language Processing荒沢康平, 狩野芳伸2024
  15. Comparison of Multilingual and Bilingual Models for Satirical News Detection of Arabic and EnglishAnnual Workshop of the Australasian Language Technology Association (ALTA)Omar W. Abdalla, Aditya Joshi, Rahat Masood, et al.2024
  16. 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)JEP-TALN-RECITAL (the French-speaking conferences on speech and natural language processing)Elie Antoine, Frederic Bechet, Géraldine Damnati, et al.2024
  17. 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)Acta Automatica Sinica (自动化学报)Qinghai Miao (缪青海), Xingxia Wang (王兴霞), Jing Yang (杨静), et al.2024

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