---
title: "Who cites Eyke Hüllermeier — 286 papers of our library"
description: "286 papers of the Elamur Research library cite the works of Eyke Hüllermeier, found in their bibliographies; the most cited first."
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---
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# Who cites Eyke Hüllermeier

287 citations of their works from 286 papers of our library (of the 149,439 whose bibliographies we read) · 68 with a page here, the most cited first · [← Eyke Hüllermeier](https://md.research.elamur.ai/free/en/authors/eyke-hullermeier/)

1. [**The Llama 3 Herd of Models arXiv:2407.21783**Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, et al.*2024 · 7,682 citations*](https://md.research.elamur.ai/free/en/papers/grattafiori-2024-llama-3-herd-models/)
2. **[A Survey on Evaluation of Large Language Models](https://md.research.elamur.ai/free/en/papers/chang-2023-survey-evaluation-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/)**Yupeng Chang, Xu Wang, Jindong Wang, et al.*2023 · 3,648 citations*
3. **[A Survey of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/) [Frontiers of Computer Science](https://md.research.elamur.ai/free/en/venues/frontiers-of-computer-science/)**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,875 citations*
4. **[A Comprehensive Overview of Large Language Models](https://md.research.elamur.ai/free/en/papers/naveed-2023-comprehensive-overview-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/)**Humza Naveed, Asad Ullah Khan, Shi Qiu, et al.*2023 · 1,287 citations*
5. [**HybridFlow: A Flexible and Efficient RLHF Framework arXiv:2409.19256**Guangming Sheng, Chi Zhang, Zilingfeng Ye, et al.*2024 · 624 citations*](https://md.research.elamur.ai/free/en/papers/sheng-2024-hybridflow-flexible-efficient-rlhf-framework/)
6. [**Cultural Bias and Cultural Alignment of Large Language Models arXiv:2311.14096 · PNAS Nexus, Volume 3, Issue 9, September 2024, pgae346**Yan Tao, Olga Viberg, Ryan S. Baker, et al.*2023 · 470 citations*](https://md.research.elamur.ai/free/en/papers/tao-2023-cultural-bias-cultural-alignment-large/)
7. **[BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset](https://md.research.elamur.ai/free/en/papers/ji-2023-beavertails-towards-improved-safety-alignment/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jiaming Ji, Mickel Liu, Juntao Dai, et al.*2023 · 362 citations*
8. [**Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning arXiv:2503.09516**Bowen Jin, Hansi Zeng, Zhenrui Yue, et al.*2025 · 322 citations*](https://md.research.elamur.ai/free/en/papers/jin-2025-search-r1-training-llms-reason/)
9. **[Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study](https://md.research.elamur.ai/free/en/papers/cao-2023-assessing-cross-cultural-alignment-between/) [Workshop on Cross-Cultural Considerations in NLP](https://md.research.elamur.ai/free/en/venues/c3nlp/)**Yong Cao, Li Zhou, Seolhwa Lee, et al.*2023 · 279 citations*
10. [**TrustLLM: Trustworthiness in Large Language Models arXiv:2401.05561**Yue Huang, Lichao Sun, Haoran Wang, et al.*2024 · 237 citations*](https://md.research.elamur.ai/free/en/papers/huang-2024-trustllm-trustworthiness-large-language-models/)
11. [**Safe RLHF: Safe Reinforcement Learning from Human Feedback arXiv:2310.12773**Josef Dai, Xuehai Pan, Ruiyang Sun, et al.*2023 · 217 citations*](https://md.research.elamur.ai/free/en/papers/dai-2023-safe-rlhf-safe-reinforcement-learning/)
12. **[Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization](https://md.research.elamur.ai/free/en/papers/tseng-2024-two-tales-persona-llms-survey/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yu-Min Tseng, Yu-Chao Huang, Teng-Yun Hsiao, et al.*2024 · 179 citations*
13. [**GPT detectors are biased against non-native English writers arXiv:2304.02819**Weixin Liang, Mert Yuksekgonul, Yining Mao, et al.*2023 · 110 citations*](https://md.research.elamur.ai/free/en/papers/liang-2023-gpt-detectors-biased-against-non/)
14. [**Large language models for education: A survey and outlook**Wang*2024 · 101 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-large-language-models-education-survey/)
15. [**ToolRL: Reward is All Tool Learning Needs arXiv:2504.13958**Cheng Qian, Emre Can Acikgoz, Qi He, et al.*2025 · 94 citations*](https://md.research.elamur.ai/free/en/papers/qian-2025-toolrl-reward-all-tool-learning/)
16. [**MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use arXiv:2310.03128**Yue Huang, Jiawen Shi, Yuan Li, et al.*2023 · 81 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-metatool-benchmark-large-language-models/)
17. [**Large Language Models in Law: A Survey arXiv:2312.03718**Jinqi Lai, Wensheng Gan, Jiayang Wu, et al.*2023 · 80 citations*](https://md.research.elamur.ai/free/en/papers/lai-2023-large-language-models-law-survey/)
18. [**Secrets of RLHF in Large Language Models Part II: Reward Modeling arXiv:2401.06080**Binghai Wang, Rui Zheng, Lu Chen, et al.*2024 · 72 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-secrets-rlhf-large-language-models/)
19. **[Are Large Language Models Capable of Generating Human-Level Narratives?](https://md.research.elamur.ai/free/en/papers/tian-2024-are-large-language-models-capable/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yufei Tian, Tenghao Huang, Miri Liu, et al.*2024 · 67 citations*
20. [**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/)
21. [**A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond arXiv:2503.21614**Xiaoye Qu, Yafu Li, Zhao-Chen Su, et al.*2025 · 55 citations*](https://md.research.elamur.ai/free/en/papers/qu-2025-survey-efficient-reasoning-large-reasoning/)
22. [**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/)
23. **[NormAd: A Framework for Measuring the Cultural Adaptability of Large Language Models](https://md.research.elamur.ai/free/en/papers/rao-2025-normad-framework-measuring-cultural-adaptability/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Abhinav Rao, Akhila Yerukola, Vishwa Shah, et al.*2025 · 46 citations*
24. **[ValueBench: Towards Comprehensively Evaluating Value Orientations and Understanding of Large Language Models](https://md.research.elamur.ai/free/en/papers/ren-2024-valuebench-comprehensively-evaluating-value-orientations/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuanyi Ren, Haoran Ye, Hanjun Fang, et al.*2024 · 43 citations*
25. [**Performance Comparison of Large Language Models on VNHSGE English Dataset: OpenAI ChatGPT, Microsoft Bing Chat, and Google Bard arXiv:2307.02288**Xuan-Quy Dao*2023 · 27 citations*](https://md.research.elamur.ai/free/en/papers/dao-2023-performance-comparison-large-language-models/)
26. **[Conformity in Large Language Models](https://md.research.elamur.ai/free/en/papers/zhu-2025-conformity-large-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaochen Zhu, Caiqi Zhang, Tom Stafford, et al.*2025 · 18 citations*
27. **[Safer or Luckier? LLMs as Safety Evaluators Are Not Robust to Artifacts](https://md.research.elamur.ai/free/en/papers/chen-2025-safer-or-luckier-llms-as/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hongyu Chen, Seraphina Goldfarb-Tarrant*2025 · 13 citations*
28. [**Know Your Audience: Do LLMs Adapt to Different Age and Education Levels? arXiv:2312.02065**Donya Rooein, Amanda Cercas Curry, Dirk Hovy*2023 · 13 citations*](https://md.research.elamur.ai/free/en/papers/rooein-2023-know-audience-llms-adapt-different/)
29. **[LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey](https://md.research.elamur.ai/free/en/papers/zou-2026-llm-based-human-agent-collaboration/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Henry Peng Zou, Wei-Chieh Huang, Yaozu Wu, et al.*2026 · 12 citations*
30. [**How well can LLMs Grade Essays in Arabic? arXiv:2501.16516**Rayed Ghazawi, Edwin Simpson*2025 · 11 citations*](https://md.research.elamur.ai/free/en/papers/ghazawi-2025-well-llms-grade-essays-arabic/)
31. [**EventRL: Enhancing Event Extraction with Outcome Supervision for Large Language Models arXiv:2402.11430**Jun Gao, Huan Zhao, Wei Wang, et al.*2024 · 11 citations*](https://md.research.elamur.ai/free/en/papers/gao-2024-eventrl-enhancing-event-extraction-outcome/)
32. [**Mixed Distillation Helps Smaller Language Model Better Reasoning arXiv:2312.10730**Chenglin Li, Qianglong Chen, Liangyue Li, et al.*2023 · 11 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-mixed-distillation-helps-smaller-language/)
33. **[A Survey of Inductive Reasoning for Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2026-survey-inductive-reasoning-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kedi Chen, Dezhao Ruan, Yuhao Dan, et al.*2026 · 10 citations*
34. [**Towards Fine-Grained Pedagogical Control over English Grammar Complexity in Educational Text Generation**Dominik Glandorf, Detmar Meurers*2024 · 7 citations*](https://md.research.elamur.ai/free/en/papers/glandorf-2024-fine-grained-pedagogical-control-over/)
35. [**Capturing the full potential of Maltese language learning through ChatGPT Technology in Language Teaching & Learning 6**Jacqueline Żammit*2024 · 6 citations*](https://md.research.elamur.ai/free/en/papers/zammit-2024-capturing-full-potential-maltese-language/)
36. **[Automatic Reviewers Fail to Detect Faulty Reasoning in Research Papers: A New Counterfactual Evaluation Framework](https://md.research.elamur.ai/free/en/papers/dycke-2026-automatic-reviewers-fail-detect-faulty/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Nils Dycke, Iryna Gurevych*2026 · 5 citations*
37. **[Can LLMs Automate Fact-Checking Article Writing?](https://md.research.elamur.ai/free/en/papers/sahnan-2026-can-llms-automate-fact-checking/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Dhruv Sahnan, David Corney, Irene Larraz, et al.*2026 · 5 citations*
38. **[AutoGraph-R1: End-to-End Reinforcement Learning for Knowledge Graph Construction](https://md.research.elamur.ai/free/en/papers/tsang-2026-autograph-r1-end-end-reinforcement/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hong Ting Tsang, Jiaxin Bai, Haoyu Huang, et al.*2026 · 4 citations*
39. **[Instructional Agents: Reducing Teaching Faculty Workload through Multi-Agent Instructional Design](https://md.research.elamur.ai/free/en/papers/yao-2026-instructional-agents-reducing-teaching-faculty/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Huaiyuan Yao, Wanpeng Xu, Justin Turnau, et al.*2026 · 4 citations*
40. **[ProMed: Shapley Information Gain Guided Reinforcement Learning for Proactive Medical LLMs](https://md.research.elamur.ai/free/en/papers/ding-2026-promed-shapley-information-gain-guided/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hongxin Ding, Baixiang Huang, Yue Fang, et al.*2026 · 4 citations*
41. **[Large Language Models For Second Language English Writing Assessments: An Exploratory Comparison](https://md.research.elamur.ai/free/en/papers/qiu-2024-large-language-models-second-language/) [Pacific Asia Conference on Language, Information and Computation (PACLIC)](https://md.research.elamur.ai/free/en/venues/paclic/)**Zhuang Qiu, Peizhi Yan, Zhenguang Cai*2024 · 3 citations*
42. **[OpenGenAlign: A Preference Dataset and Benchmark for Trustworthy Reward Modeling in Open-Ended, Long-Context Generation](https://md.research.elamur.ai/free/en/papers/zhang-2026-opengenalign-preference-dataset-benchmark-trustworthy/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hanning Zhang, Juntong Song, Juno Zhu, et al.*2026 · 2 citations*
43. [**Deciphering Emotions in Children Storybooks: A Comparative Analysis of Multimodal LLMs in Educational Applications arXiv:2506.18201**Bushra Asseri, Estabraq Abdelaziz, Maha Al Mogren, et al.*2025 · 2 citations*](https://md.research.elamur.ai/free/en/papers/asseri-2025-deciphering-emotions-children-storybooks-comparative/)
44. **[DeSegMa-IT at EVALITA 2026: Overview of the “Detection and Segmentation of Machine Generated Texts in Italian”](https://md.research.elamur.ai/free/en/papers/puccetti-2026-desegma-it-evalita-2026-overview/) [Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop](https://md.research.elamur.ai/free/en/venues/evalita/)**Giovanni Puccetti, Andrea Pedrotti, Andrea Esuli*2026 · 1 citation*
45. **[Do LLM Agents Mirror Socio-Cognitive Effects in Power-Asymmetric Conversations?](https://md.research.elamur.ai/free/en/papers/vijjini-2026-do-llm-agents-mirror-socio/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Anvesh Rao Vijjini, Sagar B. Manjunath, Snigdha Chaturvedi*2026 · 1 citation*
46. **[Position: From Noise to Signal to Selbstzweck - Reframing Human Label Variation in the Era of Post-training in NLP](https://md.research.elamur.ai/free/en/papers/xu-2026-position-noise-signal-selbstzweck-reframing/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shanshan Xu, Santosh T.Y.S.S, Barbara Plank*2026 · 1 citation*
47. **[AI-Assisted Learning in Arabic Grammar: Investigating the Use of ChatGPT and Digital Platforms in Arabic Learning](https://md.research.elamur.ai/free/en/papers/hakim-2026-ai-assisted-learning-arabic-grammar/) [Al-Ta rib Jurnal Ilmiah Program Studi Pendidikan Bahasa Arab IAIN Palangka Raya](https://md.research.elamur.ai/free/en/venues/al-ta-rib-jurnal-ilmiah-program-studi-pendidikan-bahasa-arab-iain-palangka-raya/)**Rani Ismil Hakim, Isra Hayati Darman, Ade Destri Deviana, et al.*2026*
48. **[Can LLMs Control Readability? A Multi-Dimensional Evaluation Framework for CEFR-Controlled Arabic Generation](https://md.research.elamur.ai/free/en/papers/rabih-2026-can-llms-control-readability-multi/) [Joint Workshop on Readability and Text Simplification (READIxTSAR) @ LREC](https://md.research.elamur.ai/free/en/venues/readi/)**Nour Rabih, Chatrine Qwaider, Ted Briscoe*2026*
49. **[CARL: Constraint-Aware Reinforcement Learning for Planning with LLMs](https://md.research.elamur.ai/free/en/papers/qi-2026-carl-constraint-aware-reinforcement-learning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Qiuyi Qi, Jinjian Zhang, Mutian Bao, et al.*2026*
50. **[CodeGuard: Improving LLM Guardrails in CS Education](https://md.research.elamur.ai/free/en/papers/raihan-2026-codeguard-improving-llm-guardrails-cs/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Nishat Raihan, Noah Erdachew, Jayoti Devi, et al.*2026*
51. **[Creation and Validation of a Monolingual Spanish NLI Dataset for Metaphor Interpretation via Model-in-the-Loop](https://md.research.elamur.ai/free/en/papers/montero-2026-creation-validation-monolingual-spanish-nli/) [Learning Non-Literal Expressions with Small Data @ LREC](https://md.research.elamur.ai/free/en/venues/nonliteral/)**Alec Sanchez-Montero, Gemma Bel-Enguix, Sergio Luis Ojeda Trueba*2026*
52. **[DVMap: Fine-Grained Pluralistic Value Alignment via High-Consensus Demographic-Value Mapping](https://md.research.elamur.ai/free/en/papers/zhu-2026-dvmap-fine-grained-pluralistic-value/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Pengyun Zhu, Yuqi Ren, Zhen Wang, et al.*2026*
53. **[From Answers to Arguments: Toward Trustworthy Clinical Diagnostic Reasoning with Toulmin-Guided Curriculum Goal-Conditioned Learning](https://md.research.elamur.ai/free/en/papers/zhan-2026-answers-arguments-trustworthy-clinical-diagnostic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chen Zhan, Xiaoyu Tan, Gengchen Ma, et al.*2026*
54. **[GigaCheck: Detecting LLM-generated Content via Object-Centric Span Localization](https://md.research.elamur.ai/free/en/papers/tolstykh-2026-gigacheck-detecting-llm-generated-content/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Irina Tolstykh, Aleksandra Tsybina, Sergey Yakubson, et al.*2026*
55. **[IceBreaker for Conversational Agents: Breaking the First-Message Barrier with Personalized Starters](https://md.research.elamur.ai/free/en/papers/zheng-2026-icebreaker-conversational-agents-breaking-first/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hongwei Zheng, Weiqi Wu, Zhengjia Wang, et al.*2026*
56. **[Improving LLM Domain Certification with Pretrained Guide Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-improving-llm-domain-certification-pretrained/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Jiaqian Zhang, Zhaozhi Qian, Faroq AL-Tam, et al.*2026*
57. **[Joint Optimization of Training Data and Policy in RLHF](https://md.research.elamur.ai/free/en/papers/yu-2026-joint-optimization-training-data-policy/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhuohao Yu, Jiali Zeng, Weizheng Gu, et al.*2026*
58. **[Large Language Models Are Bad Dice Players: LLMs Struggle to Generate Random Numbers from Statistical Distributions](https://md.research.elamur.ai/free/en/papers/zhao-2026-large-language-models-are-bad/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Minda Zhao, Yilun Du, Mengyu Wang*2026*
59. **[LIBERTy: A Causal Framework for Benchmarking Concept-Based Explanations of LLMs with Structural Counterfactuals](https://md.research.elamur.ai/free/en/papers/toker-2026-liberty-causal-framework-benchmarking-concept/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Gilat Toker, Nitay Calderon, Ohad Amosy, et al.*2026*
60. **[Low-Rank Compression of Language Models via Differentiable Rank Selection](https://md.research.elamur.ai/free/en/papers/sundrani-2026-low-rank-compression-language-models/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Sidhant Sundrani, Francesco Tudisco, Pasquale Minervini*2026*
61. **[Nanda Family: Open-Weights Generative Large Language Models for Hindi](https://md.research.elamur.ai/free/en/papers/singh-2026-nanda-family-open-weights-generative/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Aaryamonvikram Singh, Debopriyo Banerjee, Dhruv Sahnan, et al.*2026*
62. **[NITI: Neural Plan Concretization for Incremental Execution, Bridging and Trigger Inference from Underspecified Human Policies](https://md.research.elamur.ai/free/en/papers/banerjee-2026-niti-neural-plan-concretization-incremental/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ayan Banerjee, Shomrik Barua Banerjee, Sandeep Gupta*2026*
63. **[PARL: Prompt-based Agents for Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/resendiz-2026-parl-prompt-based-agents-reinforcement/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Yarik Menchaca Resendiz, Roman Klinger*2026*
64. **[See2Refine: Vision-Language Feedback Improves LLM-Based eHMI Action Designers](https://md.research.elamur.ai/free/en/papers/xia-2026-see2refine-vision-language-feedback-improves/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ding Xia, Xinyue Gui, Mark Colley, et al.*2026*
65. **[TeachMaster: Generative Teaching via Code](https://md.research.elamur.ai/free/en/papers/wang-2026-teachmaster-generative-teaching-code/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuheng Wang, Runde Yang, Lin Wu, et al.*2026*
66. **[Too Open for Opinion? Embracing Open-Endedness in Large Language Models for Social Simulation](https://md.research.elamur.ai/free/en/papers/ma-2026-too-open-opinion-embracing-open/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Bolei Ma, Yong Cao, Indira Sen, et al.*2026*
67. **[Which Skills Debate Reaches the Public? Comparing Scientific Literature and Media Coverage of AI and LLM Skill Impacts (2022–2025)](https://md.research.elamur.ai/free/en/papers/reveilhac-2026-which-skills-debate-reaches-public/) [Edition of the Swiss Text Analytics Conference](https://md.research.elamur.ai/free/en/venues/swisstext/)**Maud Reveilhac, Gerold Schneider, Bohdan Trembovelskyi, et al.*2026*
68. **[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)](https://md.research.elamur.ai/free/en/papers/dong-2025-code-generation-llm/) [Journal of Software](https://md.research.elamur.ai/free/en/venues/journal-of-software/)**Yihong Dong (董益宏), Xue Jiang (姜雪), Jiaru Qian (钱家如), et al.*2025*

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