---
title: "Who cites Jingqing Zhang — 868 papers of our library"
description: "868 papers of the Elamur Research library cite the works of Jingqing Zhang, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/jingqing-zhang/cited-by/"
---
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# Who cites Jingqing Zhang

868 citations of their works from 868 papers of our library (of the 149,439 whose bibliographies we read) · 80 with a page here, the most cited first · [← Jingqing Zhang](https://md.research.elamur.ai/free/en/authors/jingqing-zhang/)

1. **[Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://md.research.elamur.ai/free/en/papers/liu-2021-pre-train-prompt-predict-systematic/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Pengfei Liu, Weizhe Yuan, Jinlan Fu, et al.*2021 · 5,423 citations*
2. [**PaLM: Scaling Language Modeling with Pathways arXiv:2204.02311**Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, et al.*2022 · 4,753 citations*](https://md.research.elamur.ai/free/en/papers/chowdhery-2022-palm-scaling-language-modeling-pathways/)
3. [**Longformer: The Long-Document Transformer arXiv:2004.05150**Iz Beltagy, Matthew E. Peters, Arman Cohan*2020 · 3,880 citations*](https://md.research.elamur.ai/free/en/papers/beltagy-2020-longformer-long-document-transformer/)
4. **[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 · 3,231 citations*
5. **[TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://md.research.elamur.ai/free/en/papers/lin-2022-truthfulqa-measuring-how-models-mimic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Stephanie Lin, Jacob Hilton, Owain Evans*2022 · 2,088 citations*
6. [**Pre-trained Models for Natural Language Processing: A Survey arXiv:2003.08271 · SCIENCE CHINA Technological Sciences,2020, 63, 1872-1897**Xipeng Qiu, Tianxiang Sun, Yige Xu, et al.*2020 · 1,759 citations*](https://md.research.elamur.ai/free/en/papers/qiu-2020-pre-trained-models-natural-language/)
7. **[GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://md.research.elamur.ai/free/en/papers/du-2022-glm-general-language-model-pretraining/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhengxiao Du, Yujie Qian, Xiao Liu, et al.*2022 · 1,455 citations*
8. **[Big Bird: Transformers for Longer Sequences](https://md.research.elamur.ai/free/en/papers/zaheer-2020-big-bird-transformers-longer-sequences/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Manzil Zaheer, Guru Guruganesh, Avinava Dubey, et al.*2020 · 1,023 citations*
9. [**BARTScore: Evaluating Generated Text as Text Generation arXiv:2106.11520**Weizhe Yuan, Graham Neubig, Pengfei Liu*2021 · 815 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2021-bartscore-evaluating-generated-text-text/)
10. **[Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond](https://md.research.elamur.ai/free/en/papers/yang-2023-harnessing-power-llms-practice-survey/) [ACM Transactions on Knowledge Discovery from Data](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-knowledge-discovery-from-data/)**Jingfeng Yang, Hongye Jin, Ruixiang Tang, et al.*2023 · 814 citations*
11. **[Document Ranking with a Pretrained Sequence-to-Sequence Model](https://md.research.elamur.ai/free/en/papers/nogueira-2020-document-ranking-pretrained-sequence-sequence/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Rodrigo Nogueira, Zhiying Jiang, Ronak Pradeep, et al.*2020 · 543 citations*
12. [**OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework arXiv:2202.03052**Peng Wang, An Yang, Rui Men, et al.*2022 · 467 citations*](https://md.research.elamur.ai/free/en/papers/wang-2022-ofa-unifying-architectures-tasks-modalities/)
13. **[ProphetNet: Predicting Future N-gram for Sequence-to-SequencePre-training](https://md.research.elamur.ai/free/en/papers/qi-2020-prophetnet-predicting-future-n-gram/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Weizhen Qi, Yu Yan, Yeyun Gong, et al.*2020 · 453 citations*
14. **[LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://md.research.elamur.ai/free/en/papers/guo-2022-longt5-efficient-text-text-transformer/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mandy Guo, Joshua Ainslie, David Uthus, et al.*2022 · 399 citations*
15. **[The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics](https://md.research.elamur.ai/free/en/papers/gehrmann-2021-gem-benchmark-natural-language-generation/) [Workshop on Generation, Evaluation and Metrics (GEM)](https://md.research.elamur.ai/free/en/venues/gem/)**Sebastian Gehrmann, Tosin Adewumi, Karmanya Aggarwal, et al.*2021 · 369 citations*
16. **[BRIO: Bringing Order to Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/liu-2022-brio-bringing-order-abstractive-summarization/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yixin Liu, Pengfei Liu, Dragomir Radev, et al.*2022 · 360 citations*
17. **[QuestEval: Summarization Asks for Fact-based Evaluation](https://md.research.elamur.ai/free/en/papers/scialom-2021-questeval-summarization-asks-fact-based/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, et al.*2021 · 359 citations*
18. **[SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/liu-2021-simcls-simple-framework-contrastive-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yixin Liu, Pengfei Liu*2021 · 341 citations*
19. **[Efficient Attentions for Long Document Summarization](https://md.research.elamur.ai/free/en/papers/huang-2021-efficient-attentions-long-document-summarization/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Luyang Huang, Shuyang Cao, Nikolaus Parulian, et al.*2021 · 336 citations*
20. **[LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion](https://md.research.elamur.ai/free/en/papers/jiang-2023-llm-blender-ensembling-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dongfu Jiang, Xiang Ren, Bill Yuchen Lin*2023 · 321 citations*
21. **[From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models](https://md.research.elamur.ai/free/en/papers/feng-2023-pretraining-data-language-models-downstream/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shangbin Feng, Chan Young Park, Yuhan Liu, et al.*2023 · 311 citations*
22. **[Want To Reduce Labeling Cost? GPT-3 Can Help](https://md.research.elamur.ai/free/en/papers/wang-2021-want-reduce-labeling-cost-gpt/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shuohang Wang, Yang Liu, Yichong Xu, et al.*2021 · 300 citations*
23. **[QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarization](https://md.research.elamur.ai/free/en/papers/fabbri-2022-qafacteval-improved-qa-based-factual/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Alexander Fabbri, Chien-Sheng Wu, Wenhao Liu, et al.*2022 · 292 citations*
24. **[TLDR: Extreme Summarization of Scientific Documents](https://md.research.elamur.ai/free/en/papers/cachola-2020-tldr-extreme-summarization-scientific-documents/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Isabel Cachola, Kyle Lo, Arman Cohan, et al.*2020 · 262 citations*
25. **[On Extractive and Abstractive Neural Document Summarization with Transformer Language Models](https://md.research.elamur.ai/free/en/papers/pilault-2020-extractive-abstractive-neural-document-summarization/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jonathan Pilault, Raymond Li, Sandeep Subramanian, et al.*2020 · 261 citations*
26. **[CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/cao-2021-cliff-contrastive-learning-improving-faithfulness/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Shuyang Cao, Lu Wang*2021 · 260 citations*
27. **[Factual Error Correction for Abstractive Summarization Models](https://md.research.elamur.ai/free/en/papers/cao-2020-factual-error-correction-abstractive-summarization/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Meng Cao, Yue Dong, Jiapeng Wu, et al.*2020 · 219 citations*
28. **[Annotating and Modeling Fine-grained Factuality in Summarization](https://md.research.elamur.ai/free/en/papers/goyal-2021-annotating-modeling-fine-grained-factuality/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Tanya Goyal, Greg Durrett*2021 · 216 citations*
29. **[Entity-level Factual Consistency of Abstractive Text Summarization](https://md.research.elamur.ai/free/en/papers/nan-2021-entity-level-factual-consistency-abstractive/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Feng Nan, Ramesh Nallapati, Zhiguo Wang, et al.*2021 · 207 citations*
30. **[PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization](https://md.research.elamur.ai/free/en/papers/xiao-2022-primera-pyramid-based-masked-sentence/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wen Xiao, Iz Beltagy, Giuseppe Carenini, et al.*2022 · 205 citations*
31. **[CTRLsum: Towards Generic Controllable Text Summarization](https://md.research.elamur.ai/free/en/papers/he-2022-ctrlsum-generic-controllable-text-summarization/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Junxian He, Wojciech Kryscinski, Bryan McCann, et al.*2022 · 201 citations*
32. **[A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-safety-trustworthiness-large-language/) [Artificial Intelligence Review](https://md.research.elamur.ai/free/en/venues/artificial-intelligence-review/)**Xiaowei Huang, Wenjie Ruan, Wei Huang, et al.*2023 · 199 citations*
33. **[OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents](https://md.research.elamur.ai/free/en/papers/lauren-ccon-2023-obelics-open-web-scale-filtered/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Hugo Laurençon, Lucile Saulnier, Leo Tronchon, et al.*2023 · 197 citations*
34. **[Planning with Learned Entity Prompts for Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/narayan-2021-planning-learned-entity-prompts-abstractive/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Shashi Narayan, Yao Zhao, Joshua Maynez, et al.*2021 · 180 citations*
35. **[Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/cao-2022-hallucinated-but-factual-inspecting-factuality/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Meng Cao, Yue Dong, Jackie Cheung*2022 · 167 citations*
36. **[Few-Shot Text Generation with Natural Language Instructions](https://md.research.elamur.ai/free/en/papers/schick-2021-few-shot-text-generation-natural/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Timo Schick, Hinrich Schütze*2021 · 167 citations*
37. **[ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation](https://md.research.elamur.ai/free/en/papers/xiao-2020-ernie-gen-enhanced-multi/) [International Joint Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/international-joint-conference-on-artificial-intelligence/)**Dongling Xiao, Han Zhang, Yukun Li, et al.*2020 · 163 citations*
38. **[Multi-Fact Correction in Abstractive Text Summarization](https://md.research.elamur.ai/free/en/papers/dong-2020-multi-fact-correction-abstractive-text/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yue Dong, Shuohang Wang, Zhe Gan, et al.*2020 · 163 citations*
39. [**ChatGPT as a Factual Inconsistency Evaluator for Text Summarization arXiv:2303.15621**Zheheng Luo, Qianqian Xie, Sophia Ananiadou*2023 · 161 citations*](https://md.research.elamur.ai/free/en/papers/luo-2023-chatgpt-factual-inconsistency-evaluator-text/)
40. [**AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators arXiv:2303.16854**Xingwei He, Zhenghao Lin, Yeyun Gong, et al.*2023 · 152 citations*](https://md.research.elamur.ai/free/en/papers/he-2023-annollm-making-large-language-models/)
41. **[Many-Shot In-Context Learning](https://md.research.elamur.ai/free/en/papers/agarwal-2024-many-shot-context-learning/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Rishabh Agarwal, Avi Singh, Lei M. Zhang, et al.*2024 · 142 citations*
42. **[Structure-Aware Abstractive Conversation Summarization via Discourse and Action Graphs](https://md.research.elamur.ai/free/en/papers/chen-2021-structure-aware-abstractive-conversation-summarization/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Jiaao Chen, Diyi Yang*2021 · 135 citations*
43. **[Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error Detectors](https://md.research.elamur.ai/free/en/papers/tang-2023-understanding-factual-errors-summarization-errors/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Liyan Tang, Tanya Goyal, Alex Fabbri, et al.*2023 · 126 citations*
44. [**A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis arXiv:2307.12856**Izzeddin Gur, Hiroki Furuta, Austin Huang, et al.*2023 · 120 citations*](https://md.research.elamur.ai/free/en/papers/gur-2023-real-world-webagent-planning-long/)
45. **[Large Language Models are Not Yet Human-Level Evaluators for Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/shen-2023-large-language-models-are-not/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chenhui Shen, Liying Cheng, Xuan-Phi Nguyen, et al.*2023 · 119 citations*
46. **[Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractors](https://md.research.elamur.ai/free/en/papers/zhang-2023-aligning-instruction-tasks-unlocks-large/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kai Zhang, Bernal Jimenez Gutierrez, Yu Su*2023 · 118 citations*
47. [**Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluation arXiv:2212.07981**Yixin Liu, Alexander R. Fabbri, Pengfei Liu, et al.*2022 · 117 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-revisiting-gold-standard-grounding-summarization/)
48. **[FactPEGASUS: Factuality-Aware Pre-training and Fine-tuning for Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/wan-2022-factpegasus-factuality-aware-pre-training/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**David Wan, Mohit Bansal*2022 · 101 citations*
49. [**A Survey of Confidence Estimation and Calibration in Large Language Models arXiv:2311.08298**Jiahui Geng, Fengyu Cai, Yuxia Wang, et al.*2023 · 97 citations*](https://md.research.elamur.ai/free/en/papers/geng-2023-survey-confidence-estimation-calibration-large/)
50. **[True Few-Shot Learning with Prompts–-A Real-World Perspective](https://md.research.elamur.ai/free/en/papers/schick-2022-true-few-shot-learning-prompts/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Timo Schick, Hinrich Schütze*2022 · 96 citations*
51. **[f-Divergence Minimization for Sequence-Level Knowledge Distillation](https://md.research.elamur.ai/free/en/papers/wen-2023-f-divergence-minimization-sequence-level/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuqiao Wen, Zichao Li, Wenyu Du, et al.*2023 · 92 citations*
52. **[Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey](https://md.research.elamur.ai/free/en/papers/kumar-2023-language-generation-models-can-cause/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Sachin Kumar, Vidhisha Balachandran, Lucille Njoo, et al.*2023 · 90 citations*
53. **[CoLT5: Faster Long-Range Transformers with Conditional Computation](https://md.research.elamur.ai/free/en/papers/ainslie-2023-colt5-faster-long-range-transformers/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Joshua Ainslie, Tao Lei, Michiel de Jong, et al.*2023 · 84 citations*
54. **[ORCA: A Challenging Benchmark for Arabic Language Understanding](https://md.research.elamur.ai/free/en/papers/elmadany-2023-orca-challenging-benchmark-arabic-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**AbdelRahim Elmadany, ElMoatez Billah Nagoudi, Muhammad Abdul-Mageed*2023 · 83 citations*
55. **[Why Exposure Bias Matters: An Imitation Learning Perspective of Error Accumulation in Language Generation](https://md.research.elamur.ai/free/en/papers/arora-2022-why-exposure-bias-matters-imitation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kushal Arora, Layla El Asri, Hareesh Bahuleyan, et al.*2022 · 81 citations*
56. [**Out-of-Distribution Detection and Selective Generation for Conditional Language Models arXiv:2209.15558**Jie Ren, Jiaming Luo, Yao Zhao, et al.*2022 · 80 citations*](https://md.research.elamur.ai/free/en/papers/ren-2022-out-distribution-detection-selective-generation/)
57. **[Lift Yourself Up: Retrieval-augmented Text Generation with Self Memory](https://md.research.elamur.ai/free/en/papers/cheng-2023-lift-yourself-up-retrieval-augmented/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Xin Cheng, Di Luo, Xiuying Chen, et al.*2023 · 76 citations*
58. **[Domain Adaptation with Pre-trained Transformers for Query-Focused Abstractive Text Summarization](https://md.research.elamur.ai/free/en/papers/laskar-2022-domain-adaptation-pre-trained-transformers/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Md Tahmid Rahman Laskar, Enamul Hoque, Jimmy Xiangji Huang*2022 · 76 citations*
59. **[Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback](https://md.research.elamur.ai/free/en/papers/roit-2023-factually-consistent-summarization-reinforcement-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Paul Roit, Johan Ferret, Lior Shani, et al.*2023 · 70 citations*
60. [**Knowledge Distillation in Automated Annotation: Supervised Text Classification with LLM-Generated Training Labels arXiv:2406.17633**Nicholas Pangakis, Samuel Wolken*2024 · 42 citations*](https://md.research.elamur.ai/free/en/papers/pangakis-2024-knowledge-distillation-automated-annotation-supervised/)
61. [**Knowledge Card: Filling LLMs' Knowledge Gaps with Plug-in Specialized Language Models arXiv:2305.09955**Shangbin Feng, Weijia Shi, Yuyang Bai, et al.*2023 · 35 citations*](https://md.research.elamur.ai/free/en/papers/feng-2023-knowledge-card-filling-llms-knowledge/)
62. **[Controllable Summarization with Constrained Markov Decision Process](https://md.research.elamur.ai/free/en/papers/chan-2021-controllable-summarization-constrained-markov-decision/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Hou Pong Chan, Lu Wang, Irwin King*2021 · 34 citations*
63. [**One Law, Many Languages: Benchmarking Multilingual Legal Reasoning for Judicial Support arXiv:2306.09237**Ronja Stern, Vishvaksenan Rasiah, Veton Matoshi, et al.*2023 · 29 citations*](https://md.research.elamur.ai/free/en/papers/stern-2023-one-law-many-languages-benchmarking/)
64. **[Empathetic BERT2BERT Conversational Model: Learning Arabic Language Generation with Little Data](https://md.research.elamur.ai/free/en/papers/naous-2021-empathetic-bert2bert-conversational-model-learning/) [Arabic Natural Language Processing Workshop (WANLP)](https://md.research.elamur.ai/free/en/venues/wanlp/)**Tarek Naous, Wissam Antoun, Reem Mahmoud, et al.*2021 · 20 citations*
65. **[LexSumm and LexT5: Benchmarking and Modeling Legal Summarization Tasks in English](https://md.research.elamur.ai/free/en/papers/s-2024-lexsumm-lext5-benchmarking-modeling-legal/) [Natural Legal Language Processing Workshop](https://md.research.elamur.ai/free/en/venues/nllp/)**Santosh T.Y.S.S, Cornelius Weiss, Matthias Grabmair*2024 · 16 citations*
66. **[MDCure: A Scalable Pipeline for Multi-Document Instruction-Following](https://md.research.elamur.ai/free/en/papers/liu-2025-mdcure-scalable-pipeline-multi-document/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Gabrielle Kaili-May Liu, Bowen Shi, Avi Caciularu, et al.*2025 · 11 citations*
67. **[Factual Dialogue Summarization via Learning from Large Language Models](https://md.research.elamur.ai/free/en/papers/zhu-2025-factual-dialogue-summarization-learning-large/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Rongxin Zhu, Jey Han Lau, Jianzhong Qi*2025 · 10 citations*
68. **[LANS: Large-scale Arabic News Summarization Corpus](https://md.research.elamur.ai/free/en/papers/alhamadani-2023-lans-large-scale-arabic-news/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Abdulaziz Alhamadani, Xuchao Zhang, Jianfeng He, et al.*2023 · 7 citations*
69. **[Generating Classical Arabic Poetry using Pre-trained Models](https://md.research.elamur.ai/free/en/papers/eloraby-2022-generating-classical-arabic-poetry-pre/) [Arabic Natural Language Processing Workshop (WANLP)](https://md.research.elamur.ai/free/en/venues/wanlp/)**Maryam ElOraby, Mohamed Abdelgaber, Nehal Elkaref, et al.*2022 · 6 citations*
70. **[BitBypass: A New Direction in Jailbreaking Aligned Large Language Models with Bitstream Camouflage](https://md.research.elamur.ai/free/en/papers/nakka-2026-bitbypass-new-direction-jailbreaking-aligned/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kalyan Nakka, Nitesh Saxena*2026 · 1 citation*
71. **[Fine-Grained Detection of Context-Grounded Hallucinations Using LLMs](https://md.research.elamur.ai/free/en/papers/peisakhovsky-2026-fine-grained-detection-context-grounded/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yehonatan Peisakhovsky, Zorik Gekhman, Yosi Mass, et al.*2026 · 1 citation*
72. **[Topic-Based Watermarks for Large Language Models](https://md.research.elamur.ai/free/en/papers/nemecek-2026-topic-based-watermarks-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Alexander Nemecek, Yuzhou Jiang, Erman Ayday*2026 · 1 citation*
73. **[Beyond Overlap Metrics: Rewarding Reasoning and Preferences for Faithful Multi-Role Dialogue Summarization](https://md.research.elamur.ai/free/en/papers/mei-2026-beyond-overlap-metrics-rewarding-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiaoyong Mei, Tingting Zuo, Da Chen, et al.*2026*
74. **[Comparing Approaches to Automatic Summarization in Less-Resourced Languages](https://md.research.elamur.ai/free/en/papers/michel-2026-comparing-approaches-automatic-summarization-less/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Chester Palen-Michel, Constantine Lignos*2026*
75. **[EpiGator: An Event-based Surveillance System for Infectious Disease Outbreaks](https://md.research.elamur.ai/free/en/papers/wu-2026-epigator-event-based-surveillance-system/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Yiheng Wu, Jue Hou, Trangcasanchai Sathianpong, et al.*2026*
76. **[Evaluation of Multilingual Text Simplification for the Mental Health Domain: Exploring Small Language Models](https://md.research.elamur.ai/free/en/papers/pelloni-2026-evaluation-multilingual-text-simplification-mental/) [BioNLP](https://md.research.elamur.ai/free/en/venues/bionlp/)**Olga Pelloni, Sandra Just, Lars Bongo*2026*
77. **[From Query to Counsel: Structured Reasoning with a Multi-Agent Framework and Dataset for Legal Consultation](https://md.research.elamur.ai/free/en/papers/lu-2026-query-counsel-structured-reasoning-multi/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mingfei Lu, Yi Zhang, Mengjia Wu, et al.*2026*
78. **[Is EEG-to-Text Feasible in Real-World Scenarios? An In-Depth Analysis Using a Neuropsychology-Inspired Benchmark](https://md.research.elamur.ai/free/en/papers/zhang-2026-is-eeg-text-feasible-real/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zihan Zhang, Yu Bao, Xiao Ding, et al.*2026*
79. **[SWAN: Semantic Watermarking with Abstract Meaning Representation](https://md.research.elamur.ai/free/en/papers/ye-2026-swan-semantic-watermarking-abstract-meaning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ziping Ye, Gourab Dey, Christos Christodoulopoulos, et al.*2026*
80. **[Survey on Document-level Neural Machine Translation (文档级神经机器翻译综述)](https://md.research.elamur.ai/free/en/papers/lv-2024-document-level-neural/) [Journal of Software](https://md.research.elamur.ai/free/en/venues/journal-of-software/)**Xinglin Lv (吕星林), Junhui Li (李军辉), Shimin Tao (陶仕敏), et al.*2024*

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