---
title: "Who cites Don't Give Me the Details, Just — 978 citing papers"
description: "978 papers of the Elamur Research library cite «Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks…"
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# Who cites «Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization»

978 papers of our library cite it (of the 149,439 whose bibliographies we read) · 184 with a page here, the most cited first · page 1 of 2 · 1–100 · [← back to «Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization»](https://md.research.elamur.ai/free/en/papers/narayan-2018-don-t-give-me-details/)

**1** [2](https://md.research.elamur.ai/free/en/papers/narayan-2018-don-t-give-me-details/cited-by/page/2/) [next →](https://md.research.elamur.ai/free/en/papers/narayan-2018-don-t-give-me-details/cited-by/page/2/)

1. **[BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://md.research.elamur.ai/free/en/papers/lewis-2020-bart-denoising-sequence-sequence-pre/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mike Lewis, Yinhan Liu, Naman Goyal, et al.*2020 · 12,856 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. **[fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://md.research.elamur.ai/free/en/papers/ott-2019-fairseq-fast-extensible-toolkit-sequence/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Myle Ott, Sergey Edunov, Alexei Baevski, et al.*2019 · 3,984 citations*
4. [**Gemini: A Family of Highly Capable Multimodal Models arXiv:2312.11805**Gemini Team, Rohan Anil, Sebastian Borgeaud, et al.*2023 · 3,050 citations*](https://md.research.elamur.ai/free/en/papers/gemini-team-2023-gemini-family-highly-capable-multimodal/)
5. **[Text Summarization with Pretrained Encoders](https://md.research.elamur.ai/free/en/papers/liu-2019-text-summarization-pretrained-encoders/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yang Liu, Mirella Lapata*2019 · 2,095 citations*
6. **[G-Eval: NLG Evaluation using Gpt-4 with Better Human Alignment](https://md.research.elamur.ai/free/en/papers/liu-2023-g-eval-nlg-evaluation-gpt/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yang Liu, Dan Iter, Yichong Xu, et al.*2023 · 2,000 citations*
7. [**Finetuned Language Models Are Zero-Shot Learners arXiv:2109.01652**Jason Wei, Maarten Bosma, Vincent Y. Zhao, et al.*2021 · 1,954 citations*](https://md.research.elamur.ai/free/en/papers/wei-2021-finetuned-language-models-zero-shot/)
8. **[On Faithfulness and Factuality in Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/maynez-2020-faithfulness-factuality-abstractive-summarization/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Joshua Maynez, Shashi Narayan, Bernd Bohnet, et al.*2020 · 1,857 citations*
9. [**PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization arXiv:1912.08777**Jingqing Zhang, Yao Zhao, Mohammad Saleh, et al.*2019 · 1,828 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2019-pegasus-pre-training-extracted-gap/)
10. [**Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models arXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5Bvosj**Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al.*2022 · 1,756 citations*](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/)
11. [**Retrieval-Augmented Generation for Large Language Models: A Survey arXiv:2312.10997**Yunfan Gao, Yun Xiong, Xinyu Gao, et al.*2023 · 1,663 citations*](https://md.research.elamur.ai/free/en/papers/gao-2023-retrieval-augmented-generation-large-language/)
12. [**Multitask Prompted Training Enables Zero-Shot Task Generalization arXiv:2110.08207**Victor Sanh, Albert Webson, Colin Raffel, et al.*2021 · 1,612 citations*](https://md.research.elamur.ai/free/en/papers/sanh-2021-multitask-prompted-training-enables-zero/)
13. **[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*
14. **[Evaluating the Factual Consistency of Abstractive Text Summarization](https://md.research.elamur.ai/free/en/papers/kryscinski-2020-evaluating-factual-consistency-abstractive-text/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Wojciech Kryscinski, Bryan McCann, Caiming Xiong, et al.*2020 · 1,120 citations*
15. **[LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://md.research.elamur.ai/free/en/papers/zheng-2024-llamafactory-unified-efficient-fine-tuning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yaowei Zheng, Richong Zhang, Junhao Zhang, et al.*2024 · 1,048 citations*
16. **[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*
17. [**PaLM 2 Technical Report arXiv:2305.10403**Rohan Anil, Andrew M. Dai, Orhan Firat, et al.*2023 · 1,000 citations*](https://md.research.elamur.ai/free/en/papers/anil-2023-palm-2-technical-report/)
18. [**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/)
19. **[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*
20. **[Multi-News: A Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model](https://md.research.elamur.ai/free/en/papers/fabbri-2019-multi-news-large-scale-multi/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Alexander Fabbri, Irene Li, Tianwei She, et al.*2019 · 750 citations*
21. [**Towards a Unified View of Parameter-Efficient Transfer Learning arXiv:2110.04366**Junxian He, Chunting Zhou, Xuezhe Ma, et al.*2021 · 619 citations*](https://md.research.elamur.ai/free/en/papers/he-2021-towards-unified-view-parameter-efficient/)
22. **[Extractive Summarization as Text Matching](https://md.research.elamur.ai/free/en/papers/zhong-2020-extractive-summarization-as-text-matching/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ming Zhong, Pengfei Liu, Yiran Chen, et al.*2020 · 570 citations*
23. **[Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://md.research.elamur.ai/free/en/papers/rothe-2019-leveraging-pre-trained-checkpoints-sequence/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Sascha Rothe, Shashi Narayan, Aliaksei Severyn*2019 · 564 citations*
24. **[Neural Text Summarization: A Critical Evaluation](https://md.research.elamur.ai/free/en/papers/kryscinski-2019-neural-text-summarization-critical-evaluation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Wojciech Kryscinski, Nitish Shirish Keskar, Bryan McCann, et al.*2019 · 559 citations*
25. [**DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature arXiv:2301.11305**Eric Mitchell, Yoonho Lee, Alexander Khazatsky, et al.*2023 · 481 citations*](https://md.research.elamur.ai/free/en/papers/mitchell-2023-detectgpt-zero-shot-machine-generated/)
26. [**C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models arXiv:2305.08322**Yuzhen Huang, Yuzhuo Bai, Zhihao Zhu, et al.*2023 · 428 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-c-eval-multi-level-multi/)
27. **[Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics](https://md.research.elamur.ai/free/en/papers/pagnoni-2021-understanding-factuality-abstractive-summarization-frank/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Artidoro Pagnoni, Vidhisha Balachandran, Yulia Tsvetkov*2021 · 420 citations*
28. [**FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU arXiv:2303.06865**Ying Sheng, Lianmin Zheng, Binhang Yuan, et al.*2023 · 402 citations*](https://md.research.elamur.ai/free/en/papers/sheng-2023-flexgen-high-throughput-generative-inference/)
29. [**UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training arXiv:2002.12804**Hangbo Bao, Li Dong, Furu Wei, et al.*2020 · 395 citations*](https://md.research.elamur.ai/free/en/papers/bao-2020-unilmv2-pseudo-masked-language-models/)
30. **[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*
31. **[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*
32. **[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*
33. **[FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/durmus-2020-feqa-question-answering-evaluation-framework/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Esin Durmus, He He, Mona Diab*2020 · 359 citations*
34. [**UL2: Unifying Language Learning Paradigms arXiv:2205.05131**Yi Tay, Mostafa Dehghani, Vinh Q. Tran, et al.*2022 · 347 citations*](https://md.research.elamur.ai/free/en/papers/tay-2022-ul2-unifying-language-learning-paradigms/)
35. **[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*
36. **[GSum: A General Framework for Guided Neural Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/dou-2021-gsum-general-framework-guided-neural/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Zi-Yi Dou, Pengfei Liu, Hiroaki Hayashi, et al.*2021 · 322 citations*
37. **[Muppet: Massive Multi-task Representations with Pre-Finetuning](https://md.research.elamur.ai/free/en/papers/aghajanyan-2021-muppet-massive-multi-task-representations/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Armen Aghajanyan, Anchit Gupta, Akshat Shrivastava, et al.*2021 · 307 citations*
38. [**Can AI-Generated Text be Reliably Detected? arXiv:2303.11156**Vinu Sankar Sadasivan, Aounon Kumar, Sriram Balasubramanian, et al.*2023 · 306 citations*](https://md.research.elamur.ai/free/en/papers/sadasivan-2023-ai-generated-text-reliably-detected/)
39. **[SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer](https://md.research.elamur.ai/free/en/papers/vu-2022-spot-better-frozen-model-adaptation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tu Vu, Brian Lester, Noah Constant, et al.*2022 · 306 citations*
40. **[DialogSum: A Real-Life Scenario Dialogue Summarization Dataset](https://md.research.elamur.ai/free/en/papers/chen-2021-dialogsum-real-life-scenario-dialogue/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yulong Chen, Yang Liu, Liang Chen, et al.*2021 · 301 citations*
41. **[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*
42. [**On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes arXiv:2306.13649**Rishabh Agarwal, Nino Vieillard, Yongchao Zhou, et al.*2023 · 299 citations*](https://md.research.elamur.ai/free/en/papers/agarwal-2023-policy-distillation-language-models-learning/)
43. **[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*
44. **[TRUE: Re-evaluating Factual Consistency Evaluation](https://md.research.elamur.ai/free/en/papers/honovich-2022-true-re-evaluating-factual-consistency/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Or Honovich, Roee Aharoni, Jonathan Herzig, et al.*2022 · 279 citations*
45. **[A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions](https://md.research.elamur.ai/free/en/papers/wu-2025-survey-llm-generated-text-detection/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Junchao Wu, Shu Yang, Runzhe Zhan, et al.*2025 · 277 citations*
46. **[WikiLingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/ladhak-2020-wikilingua-new-benchmark-dataset-cross/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Faisal Ladhak, Esin Durmus, Claire Cardie, et al.*2020 · 275 citations*
47. **[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*
48. **[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*
49. [**Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model arXiv:2402.07827**Ahmet Üstün, Viraat Aryabumi, Zheng-Xin Yong, et al.*2024 · 253 citations*](https://md.research.elamur.ai/free/en/papers/ustun-2024-aya-model-instruction-finetuned-open/)
50. **[Sentence Centrality Revisited for Unsupervised Summarization](https://md.research.elamur.ai/free/en/papers/zheng-2019-sentence-centrality-revisited-unsupervised-summarization/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hao Zheng, Mirella Lapata*2019 · 251 citations*
51. **[Trusting Your Evidence: Hallucinate Less with Context-aware Decoding](https://md.research.elamur.ai/free/en/papers/shi-2024-trusting-your-evidence-hallucinate-less/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Weijia Shi, Xiaochuang Han, Mike Lewis, et al.*2024 · 249 citations*
52. **[BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization](https://md.research.elamur.ai/free/en/papers/sharma-2019-bigpatent-large-scale-dataset-abstractive/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Eva Sharma, Chen Li, Lu Wang*2019 · 236 citations*
53. **[Detecting Hallucinated Content in Conditional Neural Sequence Generation](https://md.research.elamur.ai/free/en/papers/zhou-2021-detecting-hallucinated-content-conditional-neural/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chunting Zhou, Graham Neubig, Jiatao Gu, et al.*2021 · 232 citations*
54. **[The elephant in the interpretability room: Why use attention as explanation when we have saliency methods?](https://md.research.elamur.ai/free/en/papers/bastings-2020-elephant-interpretability-room-why-use/) [BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP](https://md.research.elamur.ai/free/en/venues/blackboxnlp/)**Jasmijn Bastings, Katja Filippova*2020 · 219 citations*
55. **[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*
56. **[CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP](https://md.research.elamur.ai/free/en/papers/ye-2021-crossfit-few-shot-learning-challenge/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Qinyuan Ye, Bill Yuchen Lin, Xiang Ren*2021 · 210 citations*
57. **[Towards a Unified Multi-Dimensional Evaluator for Text Generation](https://md.research.elamur.ai/free/en/papers/zhong-2022-unified-multi-dimensional-evaluator-text/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ming Zhong, Yang Liu, Da Yin, et al.*2022 · 208 citations*
58. **[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*
59. **[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*
60. [**LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods arXiv:2412.05579**Haitao Li, Qian Dong, Junjie Chen, et al.*2024 · 195 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-llms-judges-comprehensive-survey-llm/)
61. [**One Embedder, Any Task: Instruction-Finetuned Text Embeddings arXiv:2212.09741**Hongjin Su, Weijia Shi, Jungo Kasai, et al.*2022 · 193 citations*](https://md.research.elamur.ai/free/en/papers/su-2022-one-embedder-any-task-instruction/)
62. **[Compressing Large-Scale Transformer-Based Models: A Case Study on BERT](https://md.research.elamur.ai/free/en/papers/ganesh-2021-compressing-large-scale-transformer-based/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Prakhar Ganesh, Yao Chen, Xin Lou, et al.*2021 · 190 citations*
63. **[Enhancing Factual Consistency of Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/zhu-2021-enhancing-factual-consistency-abstractive-summarization/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Chenguang Zhu, William Hinthorn, Ruochen Xu, et al.*2021 · 190 citations*
64. **[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*
65. [**AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning arXiv:2303.10512**Qingru Zhang, Minshuo Chen, Alexander Bukharin, et al.*2023 · 173 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-adalora-adaptive-budget-allocation-parameter/)
66. [**Generative Representational Instruction Tuning arXiv:2402.09906**Niklas Muennighoff, Hongjin Su, Liang Wang, et al.*2024 · 172 citations*](https://md.research.elamur.ai/free/en/papers/muennighoff-2024-generative-representational-instruction-tuning/)
67. **[DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text](https://md.research.elamur.ai/free/en/papers/su-2023-detectllm-leveraging-log-rank-information/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jinyan Su, Terry Zhuo, Di Wang, et al.*2023 · 172 citations*
68. **[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*
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88. [**Time Travel in LLMs: Tracing Data Contamination in Large Language Models arXiv:2308.08493**Shahriar Golchin, Mihai Surdeanu*2023 · 128 citations*](https://md.research.elamur.ai/free/en/papers/golchin-2023-time-travel-llms-tracing-data/)
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