---
title: "Who cites Holy Lovenia — 768 papers of our library"
description: "768 papers of the Elamur Research library cite the works of Holy Lovenia, found in their bibliographies · page 1 of 2 · 1–100; the most cited first."
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---
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# Who cites Holy Lovenia

823 citations of their works from 768 papers of our library (of the 141,840 whose bibliographies we read) · 105 with a page here, the most cited first · page 1 of 2 · 1–100 · [← Holy Lovenia](https://md.research.elamur.ai/free/en/authors/holy-lovenia/)

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1. **[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 · 3,641 citations*
2. **[A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-hallucination-large-language-models/) [ACM Transactions on Information Systems](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-information-systems/)**Lei Huang, Weijiang Yu, Weitao Ma, et al.*2023 · 3,222 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 · 2,863 citations*
4. **[Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://md.research.elamur.ai/free/en/papers/pan-2023-unifying-large-language-models-knowledge/) [IEEE Transactions on Knowledge and Data Engineering](https://md.research.elamur.ai/free/en/venues/ieee-transactions-on-knowledge-and-data-engineering/)**Shirui Pan, Linhao Luo, Yufei Wang, et al.*2023 · 1,549 citations*
5. **[The Rise and Potential of Large Language Model Based Agents: A Survey](https://md.research.elamur.ai/free/en/papers/xi-2023-rise-potential-large-language/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Zhiheng Xi, Wenxiang Chen, Xin Guo, et al.*2023 · 1,264 citations*
6. **[Evaluating Object Hallucination in Large Vision-Language Models](https://md.research.elamur.ai/free/en/papers/li-2023-evaluating-object-hallucination-large-vision/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yifan Li, Yifan Du, Kun Zhou, et al.*2023 · 986 citations*
7. **[Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2023-siren-s-song-ai-ocean/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Yue Zhang, Yafu Li, Leyang Cui, et al.*2023 · 966 citations*
8. [**Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models arXiv:2304.01852 · Meta-Radiology (2023)100,017**Yiheng Liu, Tianle Han, Siyuan Ma, et al.*2023 · 938 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-summary-chatgpt-related-research-perspective/)
9. [**A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT arXiv:2302.11382**Jules White, Quchen Fu, Sam Hays, et al.*2023 · 902 citations*](https://md.research.elamur.ai/free/en/papers/white-2023-prompt-pattern-catalog-enhance-prompt/)
10. [**ChatGPT: Jack of all trades, master of none arXiv:2302.10724 · Information Fusion 101,861 (2023)**Jan Kocon, Igor Cichecki, Oliwier Kaszyca, et al.*2023 · 865 citations*](https://md.research.elamur.ai/free/en/papers/kocon-2023-chatgpt-jack-all-trades-master/)
11. **[Towards Reasoning in Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/huang-2023-reasoning-large-language-models-survey/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jie Huang, Kevin Chen-Chuan Chang*2023 · 778 citations*
12. **[Can Large Language Models Transform Computational Social Science?](https://md.research.elamur.ai/free/en/papers/ziems-2023-large-language-models-transform-computational/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Caleb Ziems, William Held, Omar Shaikh, et al.*2023 · 777 citations*
13. [**Crosslingual Generalization through Multitask Finetuning arXiv:2211.01786**Niklas Muennighoff, Thomas Wang, Lintang Sutawika, et al.*2022 · 695 citations*](https://md.research.elamur.ai/free/en/papers/muennighoff-2022-crosslingual-generalization-through-multitask-finetuning/)
14. **[HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models](https://md.research.elamur.ai/free/en/papers/li-2023-halueval-large-scale-hallucination-evaluation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Junyi Li, Xiaoxue Cheng, Wayne Xin Zhao, et al.*2023 · 647 citations*
15. [**ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge arXiv:2303.14070**Yunxiang Li, Zihan Li, Kai Zhang, et al.*2023 · 643 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-chatdoctor-medical-chat-model-fine/)
16. **[Benchmarking Large Language Models in Retrieval-Augmented Generation](https://md.research.elamur.ai/free/en/papers/chen-2023-benchmarking-large-language-models-retrieval/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Jiawei Chen, Hongyu Lin, Xianpei Han, et al.*2023 · 625 citations*
17. **[Sentiment Analysis in the Era of Large Language Models: A Reality Check](https://md.research.elamur.ai/free/en/papers/zhang-2023-sentiment-analysis-era-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wenxuan Zhang, Yue Deng, Bing Liu, et al.*2023 · 505 citations*
18. **[Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis](https://md.research.elamur.ai/free/en/papers/zhu-2024-multilingual-machine-translation-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wenhao Zhu, Hongyi Liu, Qingxiu Dong, et al.*2024 · 503 citations*
19. [**Large Language Models Cannot Self-Correct Reasoning Yet arXiv:2310.01798**Jie Huang, Xinyun Chen, Swaroop Mishra, et al.*2023 · 396 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-large-language-models-cannot-self/)
20. **[Query Rewriting in Retrieval-Augmented Large Language Models](https://md.research.elamur.ai/free/en/papers/ma-2023-query-rewriting-retrieval-augmented-large/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xinbei Ma, Yeyun Gong, Pengcheng He, et al.*2023 · 383 citations*
21. **[Towards Mitigating LLM Hallucination via Self Reflection](https://md.research.elamur.ai/free/en/papers/ji-2023-mitigating-llm-hallucination-self-reflection/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ziwei Ji, Tiezheng Yu, Yan Xu, et al.*2023 · 352 citations*
22. **[Toxicity in chatgpt: Analyzing persona-assigned language models](https://md.research.elamur.ai/free/en/papers/deshpande-2023-toxicity-chatgpt-analyzing-persona-assigned/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ameet Deshpande, Vishvak Murahari, Tanmay Rajpurohit, et al.*2023 · 346 citations*
23. [**"Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models arXiv:2308.03825**Xinyue Shen, Zeyuan Chen, Michael Backes, et al.*2023 · 320 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-anything-now-characterizing-evaluating-wild/)
24. **[AugGPT: Leveraging ChatGPT for Text Data Augmentation](https://md.research.elamur.ai/free/en/papers/dai-2023-auggpt-leveraging-chatgpt-text-data/) [IEEE Transactions on Big Data](https://md.research.elamur.ai/free/en/venues/ieee-transactions-on-big-data/)**Haixing Dai; Zhengliang Liu; Wenxiong Liao; Xiaoke Huang; Yihan Cao; Zihao Wu; Lin Zhao; Shaochen Xu; Wei Liu; Ninghao Liu; Sheng Li; Dajiang Zhu; Hongmin Cai; Lichao Sun; Quanzheng Li; Dinggang Shen; Tianming Liu;, Xiang Li*2023 · 306 citations*
25. [**Is ChatGPT a Good NLG Evaluator? A Preliminary Study arXiv:2303.04048**Jiaan Wang, Yunlong Liang, Fandong Meng, et al.*2023 · 282 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-chatgpt-good-nlg-evaluator-preliminary/)
26. [**MEGA: Multilingual Evaluation of Generative AI arXiv:2303.12528**Kabir Ahuja, Harshita Diddee, Rishav Hada, et al.*2023 · 282 citations*](https://md.research.elamur.ai/free/en/papers/ahuja-2023-mega-multilingual-evaluation-generative-ai/)
27. **[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 · 278 citations*
28. [**Corrective Retrieval Augmented Generation arXiv:2401.15884**Shi-Qi Yan, Jia-Chen Gu, Yun Zhu, et al.*2024 · 271 citations*](https://md.research.elamur.ai/free/en/papers/yan-2024-corrective-retrieval-augmented-generation/)
29. **[Factuality Challenges in the Era of Large Language Models](https://md.research.elamur.ai/free/en/papers/augenstein-2023-factuality-challenges-era-large-language/) [Nature Machine Intelligence](https://md.research.elamur.ai/free/en/venues/nature-machine-intelligence/)**Isabelle Augenstein, Timothy Baldwin, Meeyoung Cha, et al.*2023 · 242 citations*
30. **[On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey](https://md.research.elamur.ai/free/en/papers/long-2024-llms-driven-synthetic-data-generation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Lin Long, Rui Wang, Ruixuan Xiao, et al.*2024 · 236 citations*
31. [**ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning arXiv:2304.06588**Petter Tornberg*2023 · 231 citations*](https://md.research.elamur.ai/free/en/papers/tornberg-2023-chatgpt-4-outperforms-experts-crowd/)
32. [**Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting arXiv:2305.07004**Haoyang Huang, Tianyi Tang, Dongdong Zhang, et al.*2023 · 224 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-all-languages-created-equal-llms/)
33. [**Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment arXiv:2308.05374**Yang Liu; Yuanshun Yao; Jean-Francois Ton; Xiaoying Zhang; Ruocheng Guo; Hao Cheng; Yegor Klochkov; Muhammad Faaiz Taufiq;, Hang Li*2023 · 220 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-trustworthy-llms-survey-guideline-evaluating/)
34. [**Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4 arXiv:2304.03439**Hanmeng Liu, Ruoxi Ning, Zhiyang Teng, et al.*2023 · 215 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-evaluating-logical-reasoning-ability-chatgpt/)
35. **[Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning](https://md.research.elamur.ai/free/en/papers/singh-2024-aya-dataset-open-access-collection/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shivalika Singh, Freddie Vargus, Daniel D'souza, et al.*2024 · 203 citations*
36. **[A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets](https://md.research.elamur.ai/free/en/papers/laskar-2023-systematic-study-comprehensive-evaluation-chatgpt/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Md Tahmid Rahman Laskar, M Saiful Bari, Mizanur Rahman, et al.*2023 · 200 citations*
37. **[Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias](https://md.research.elamur.ai/free/en/papers/yu-2023-large-language-model-attributed-training/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yue Yu, Yuchen Zhuang, Jieyu Zhang, et al.*2023 · 198 citations*
38. **[Language Model Tokenizers Introduce Unfairness Between Languages](https://md.research.elamur.ai/free/en/papers/petrov-2023-language-model-tokenizers-introduce-unfairness/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Aleksandar Petrov, Emanuele La Malfa, Philip H. S. Torr, et al.*2023 · 197 citations*
39. **[Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback](https://md.research.elamur.ai/free/en/papers/lai-2023-okapi-instruction-tuned-large-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Viet Lai, Chien Nguyen, Nghia Ngo, et al.*2023 · 194 citations*
40. [**The Prompt Report: A Systematic Survey of Prompt Engineering Techniques arXiv:2406.06608**Sander Schulhoff, Michael Ilie, Nishant Balepur, et al.*2024 · 187 citations*](https://md.research.elamur.ai/free/en/papers/schulhoff-2024-prompt-report-systematic-survey-prompt/)
41. [**Multilingual Jailbreak Challenges in Large Language Models arXiv:2310.06474**Yue Deng, Wenxuan Zhang, Sinno Jialin Pan, et al.*2023 · 183 citations*](https://md.research.elamur.ai/free/en/papers/deng-2023-multilingual-jailbreak-challenges-large-language/)
42. **[CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages](https://md.research.elamur.ai/free/en/papers/nguyen-2023-culturax-cleaned-enormous-multilingual-dataset/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Thuat Nguyen, Chien Van Nguyen, Viet Dac Lai, et al.*2023 · 180 citations*
43. [**Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning arXiv:2306.14565**Fuxiao Liu, Kevin Lin, Linjie Li, et al.*2023 · 180 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-mitigating-hallucination-large-multi-modal/)
44. **[Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation](https://md.research.elamur.ai/free/en/papers/singh-2025-global-mmlu-understanding-addressing-cultural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shivalika Singh, Angelika Romanou, Clémentine Fourrier, et al.*2025 · 179 citations*
45. [**Evaluating Large Language Models: A Comprehensive Survey arXiv:2310.19736**Zishan Guo, Renren Jin, Chuang Liu, et al.*2023 · 178 citations*](https://md.research.elamur.ai/free/en/papers/guo-2023-evaluating-large-language-models-comprehensive/)
46. **[Do Llamas Work in English? On the Latent Language of Multilingual Transformers](https://md.research.elamur.ai/free/en/papers/wendler-2024-do-llamas-work-english-latent/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chris Wendler, Veniamin Veselovsky, Giovanni Monea, et al.*2024 · 170 citations*
47. [**Low-Resource Languages Jailbreak GPT-4 arXiv:2310.02446**Zheng-Xin Yong, Cristina Menghini, Stephen H. Bach*2023 · 168 citations*](https://md.research.elamur.ai/free/en/papers/yong-2023-low-resource-languages-jailbreak-gpt/)
48. [**ChatGPT as a Factual Inconsistency Evaluator for Text Summarization arXiv:2303.15621**Zheheng Luo, Qianqian Xie, Sophia Ananiadou*2023 · 160 citations*](https://md.research.elamur.ai/free/en/papers/luo-2023-chatgpt-factual-inconsistency-evaluator-text/)
49. **[ChatGPT MT: Competitive for High- (but Not Low-) Resource Languages](https://md.research.elamur.ai/free/en/papers/robinson-2023-chatgpt-mt-competitive-high-but/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Nathaniel Robinson, Perez Ogayo, David R. Mortensen, et al.*2023 · 151 citations*
50. [**Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigation arXiv:2305.15852**Niels Mundler, Jingxuan He, Slobodan Jenko, et al.*2023 · 150 citations*](https://md.research.elamur.ai/free/en/papers/mundler-2023-self-contradictory-hallucinations-large-language/)
51. [**Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge arXiv:2410.02736**Jiayi Ye, Yanbo Wang, Yue Huang, et al.*2024 · 142 citations*](https://md.research.elamur.ai/free/en/papers/ye-2024-justice-prejudice-quantifying-biases-llm/)
52. **[Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyond](https://md.research.elamur.ai/free/en/papers/xu-2023-large-language-models-really-good/) [IEEE Transactions on Knowledge and Data Engineering](https://md.research.elamur.ai/free/en/venues/ieee-transactions-on-knowledge-and-data-engineering/)**Fangzhi Xu, Qika Lin, Jiawei Han, et al.*2023 · 141 citations*
53. [**Exploring the Limits of ChatGPT for Query or Aspect-based Text Summarization arXiv:2302.08081**Xianjun Yang, Yan Li, Xinlu Zhang, et al.*2023 · 141 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-exploring-limits-chatgpt-query-aspect/)
54. [**Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration arXiv:2307.05300**Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, et al.*2023 · 139 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-unleashing-emergent-cognitive-synergy-large/)
55. **[InFoBench: Evaluating Instruction Following Ability in Large Language Models](https://md.research.elamur.ai/free/en/papers/qin-2024-infobench-evaluating-instruction-following-ability/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yiwei Qin, Kaiqiang Song, Yebowen Hu, et al.*2024 · 133 citations*
56. **[Prompting and Evaluating Large Language Models for Proactive Dialogues: Clarification, Target-guided, and Non-collaboration](https://md.research.elamur.ai/free/en/papers/deng-2023-prompting-evaluating-large-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yang Deng, Lizi Liao, Liang Chen, et al.*2023 · 128 citations*
57. [**Time Travel in LLMs: Tracing Data Contamination in Large Language Models arXiv:2308.08493**Shahriar Golchin, Mihai Surdeanu*2023 · 127 citations*](https://md.research.elamur.ai/free/en/papers/golchin-2023-time-travel-llms-tracing-data/)
58. [**Exploring the Feasibility of ChatGPT for Event Extraction arXiv:2303.03836**Jun Gao, Huan Zhao, Changlong Yu, et al.*2023 · 115 citations*](https://md.research.elamur.ai/free/en/papers/gao-2023-exploring-feasibility-chatgpt-event-extraction/)
59. **[GPTAraEval: A Comprehensive Evaluation of ChatGPT on Arabic NLP](https://md.research.elamur.ai/free/en/papers/khondaker-2023-gptaraeval-comprehensive-evaluation-chatgpt-arabic/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Md Tawkat Islam Khondaker, Abdul Waheed, El Moatez Billah Nagoudi, et al.*2023 · 110 citations*
60. **[ChatGPT Is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models](https://md.research.elamur.ai/free/en/papers/bian-2024-chatgpt-is-knowledgeable-but-inexperienced/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Ning Bian, Xianpei Han, Le Sun, et al.*2024 · 105 citations*
61. **[Survey of Cultural Awareness in Language Models: Text and Beyond](https://md.research.elamur.ai/free/en/papers/pawar-2025-survey-cultural-awareness-language-models/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Siddhesh Pawar, Junyeong Park, Jiho Jin, et al.*2025 · 104 citations*
62. [**MMTEB: Massive Multilingual Text Embedding Benchmark arXiv:2502.13595**Kenneth Enevoldsen, Isaac Chung, Imene Kerboua, et al.*2025 · 101 citations*](https://md.research.elamur.ai/free/en/papers/enevoldsen-2025-mmteb-massive-multilingual-text-embedding/)
63. **[CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotation](https://md.research.elamur.ai/free/en/papers/li-2023-coannotating-uncertainty-guided-work-allocation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Minzhi Li, Taiwei Shi, Caleb Ziems, et al.*2023 · 98 citations*
64. **[Not All Countries Celebrate Thanksgiving: On the Cultural Dominance in Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2024-not-all-countries-celebrate-thanksgiving/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wenxuan Wang, Wenxiang Jiao, Jingyuan Huang, et al.*2024 · 96 citations*
65. **[Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification](https://md.research.elamur.ai/free/en/papers/fadeeva-2024-fact-checking-output-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ekaterina Fadeeva, Aleksandr Rubashevskii, Artem Shelmanov, et al.*2024 · 95 citations*
66. **[Exploring the Potential of ChatGPT on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations](https://md.research.elamur.ai/free/en/papers/chan-2024-exploring-potential-chatgpt-sentence-level/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chunkit Chan, Cheng Jiayang, Weiqi Wang, et al.*2024 · 92 citations*
67. **[LLMs Are Few-Shot In-Context Low-Resource Language Learners](https://md.research.elamur.ai/free/en/papers/cahyawijaya-2024-llms-are-few-shot-context/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Samuel Cahyawijaya, Holy Lovenia, Pascale Fung*2024 · 92 citations*
68. **[Evaluation Metrics in the Era of GPT-4: Reliably Evaluating Large Language Models on Sequence to Sequence Tasks](https://md.research.elamur.ai/free/en/papers/sottana-2023-evaluation-metrics-era-gpt-4/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Andrea Sottana, Bin Liang, Kai Zou, et al.*2023 · 89 citations*
69. **[Benchmarking Vision Language Models for Cultural Understanding](https://md.research.elamur.ai/free/en/papers/nayak-2024-benchmarking-vision-language-models-cultural/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Shravan Nayak, Kanishk Jain, Rabiul Awal, et al.*2024 · 85 citations*
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71. [**Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks arXiv:2310.10844**Erfan Shayegani, Md Abdullah Al Mamun, Yu Fu, et al.*2023 · 83 citations*](https://md.research.elamur.ai/free/en/papers/shayegani-2023-survey-vulnerabilities-large-language-models/)
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76. [**Exploring the Impact of Instruction Data Scaling on Large Language Models: An Empirical Study on Real-World Use Cases arXiv:2303.14742**Yunjie Ji, Yong Deng, Yan Gong, et al.*2023 · 62 citations*](https://md.research.elamur.ai/free/en/papers/ji-2023-exploring-impact-instruction-data/)
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79. [**ChatGPT vs Human-authored Text: Insights into Controllable Text Summarization and Sentence Style Transfer arXiv:2306.07799**Dongqi Liu, Vera Demberg*2023 · 43 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-chatgpt-vs-human-authored-text/)
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82. [**Taqyim: Evaluating Arabic NLP Tasks Using ChatGPT Models arXiv:2306.16322**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/)
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87. [**Constituency Parsing using LLMs arXiv:2310.19462**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/)
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90. [**OASIS: A Multilingual and Multimodal Dataset for Culturally Grounded Spoken Visual QA arXiv:2510.06371**Firoj Alam, Ali Ezzat Shahroor, Md. Arid Hasan, et al.*2025 · 6 citations*](https://md.research.elamur.ai/free/en/papers/alam-2025-oasis-multilingual-multimodal-dataset-culturally/)
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95. [**Rosetta at AlexandriaX-2026: LoRA-Adapted NileChat for Context-Aware Dialectal Arabic Dialogue Translation arXiv:2609.10395**Nada Esmaeil, Fathima Rena, Sibi Subhash, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/esmaeil-2026-rosetta-alexandriax-2026-lora-adapted/)
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100. [**Figurative and Cultural Knowledge in LLMs: Investigating Cross-Domain Transfer through Fine-Tuning arXiv:2608.18361**Mena Attia, Mona Diab, Thamar Solorio*2026*](https://md.research.elamur.ai/free/en/papers/attia-2026-figurative-cultural-knowledge-llms-investigating/)

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