---
title: "Who cites Adam K. Lerer — 7,629 papers of our library"
description: "7,629 papers of the Elamur Research library cite the works of Adam K. Lerer, found in their bibliographies · page 1 of 13 · 1–100; the most cited first."
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---
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# Who cites Adam K. Lerer

7,965 citations of their works from 7,629 papers of our library (of the 149,439 whose bibliographies we read) · 1,237 with a page here, the most cited first · page 1 of 13 · 1–100 · [← Adam K. Lerer](https://md.research.elamur.ai/free/en/authors/adam-k-lerer/)

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1. [**PyTorch: An Imperative Style, High-Performance Deep Learning Library arXiv:1912.01703**Adam Paszke, Sam Gross, Francisco Massa, et al.*2019 · 18,645 citations*](https://md.research.elamur.ai/free/en/papers/paszke-2019-pytorch-imperative-style-high-performance/)
2. [**RoBERTa: A Robustly Optimized BERT Pretraining Approach arXiv:1907.11692**Yinhan Liu, Myle Ott, Naman Goyal, et al.*2019 · 9,837 citations*](https://md.research.elamur.ai/free/en/papers/liu-2019-roberta-robustly-optimized-bert-pretraining/)
3. [**Learning Transferable Visual Models From Natural Language Supervision arXiv:2103.00020**Alec Radford, Jong Wook Kim, Chris Hallacy, et al.*2021 · 8,767 citations*](https://md.research.elamur.ai/free/en/papers/radford-2021-learning-transferable-visual-models-natural/)
4. **[Large language models are zero-shot reasoners](https://md.research.elamur.ai/free/en/papers/kojima-2022-large-language-models-zero-shot/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Takeshi Kojima, Shixiang Shane Gu, Machel Reid, et al.*2022 · 3,832 citations*
5. [**On the Opportunities and Risks of Foundation Models arXiv:2108.07258**Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.*2021 · 3,160 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/)
6. **[SuperGlue: Learning Feature Matching with Graph Neural Networks](https://md.research.elamur.ai/free/en/papers/sarlin-2019-superglue-learning-feature-matching-graph/) [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/)**Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, et al.*2019 · 2,921 citations*
7. **[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*
8. [**Robust Speech Recognition via Large-Scale Weak Supervision arXiv:2212.04356**Alec Radford, Jong Wook Kim, Tao Xu, et al.*2022 · 2,546 citations*](https://md.research.elamur.ai/free/en/papers/radford-2022-robust-speech-recognition-large-scale/)
9. **[UNITER: UNiversal Image-TExt Representation Learning](https://md.research.elamur.ai/free/en/papers/chen-2019-uniter-universal-image-text-representation/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Yen-Chun Chen, Linjie Li, Licheng Yu, et al.*2019 · 2,418 citations*
10. [**SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems arXiv:1905.00537**Alex Wang, Yada Pruksachatkun, Nikita Nangia, et al.*2019 · 2,227 citations*](https://md.research.elamur.ai/free/en/papers/wang-2019-superglue-stickier-benchmark-general-purpose/)
11. [**AllenNLP: A Deep Semantic Natural Language Processing Platform arXiv:1803.07640**Matt Gardner, Joel Grus, Mark Neumann, et al.*2018 · 1,819 citations*](https://md.research.elamur.ai/free/en/papers/gardner-2018-allennlp-deep-semantic-natural-language/)
12. **[Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference](https://md.research.elamur.ai/free/en/papers/schick-2021-exploiting-cloze-questions-few-shot/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Timo Schick, Hinrich Schütze*2021 · 1,817 citations*
13. [**BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation arXiv:2201.12086**Junnan Li, Dongxu Li, Caiming Xiong, et al.*2022 · 1,523 citations*](https://md.research.elamur.ai/free/en/papers/li-2022-blip-bootstrapping-language-image-pre/)
14. [**Fast Graph Representation Learning with PyTorch Geometric arXiv:1903.02428**Matthias Fey, Jan Eric Lenssen*2019 · 1,440 citations*](https://md.research.elamur.ai/free/en/papers/fey-2019-fast-graph-representation-learning-pytorch/)
15. **[FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search](https://md.research.elamur.ai/free/en/papers/wu-2018-fbnet-hardware-aware-efficient-convnet/) [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/)**Bichen Wu, Xiaoliang Dai, Peizhao Zhang, et al.*2018 · 1,405 citations*
16. **[RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models](https://md.research.elamur.ai/free/en/papers/gehman-2020-realtoxicityprompts-evaluating-neural-toxic-degeneration/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Samuel Gehman, Suchin Gururangan, Maarten Sap, et al.*2020 · 1,306 citations*
17. **[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*
18. **[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 · 1,272 citations*
19. **[A Structural Probe for Finding Syntax in Word Representations](https://md.research.elamur.ai/free/en/papers/hewitt-2019-structural-probe-finding-syntax-word/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**John Hewitt, Christopher D. Manning*2019 · 1,254 citations*
20. **[It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners](https://md.research.elamur.ai/free/en/papers/schick-2021-it-s-not-just-size/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Timo Schick, Hinrich Schütze*2021 · 1,132 citations*
21. **[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*
22. **[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*
23. **[PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation](https://md.research.elamur.ai/free/en/papers/ansel-2024-pytorch-2-faster-machine-learning/) [ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)](https://md.research.elamur.ai/free/en/venues/acm-international-conference-on-architectural-support-for-programming-languages/)**Jason Ansel, Edward Yang, Horace He, et al.*2024 · 946 citations*
24. **[4D Gaussian Splatting for Real-Time Dynamic Scene Rendering](https://md.research.elamur.ai/free/en/papers/wu-2023-4d-gaussian-splatting-real-time/) [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/)**Guanjun Wu, Taoran Yi, Jiemin Fang, et al.*2023 · 887 citations*
25. [**Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution arXiv:2409.12191**Peng Wang, Shuai Bai, Sinan Tan, et al.*2024 · 860 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-qwen2-vl-enhancing-vision/)
26. **[ERASER: A Benchmark to Evaluate Rationalized NLP Models](https://md.research.elamur.ai/free/en/papers/deyoung-2020-eraser-benchmark-evaluate-rationalized-nlp/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, et al.*2020 · 832 citations*
27. [**TuckER: Tensor Factorization for Knowledge Graph Completion arXiv:1901.09590**Ivana Balažević, Carl Allen, Timothy M. Hospedales*2019 · 799 citations*](https://md.research.elamur.ai/free/en/papers/balazevic-2019-tucker-tensor-factorization-knowledge-graph/)
28. **[Reproducible scaling laws for contrastive language-image learning](https://md.research.elamur.ai/free/en/papers/cherti-2022-reproducible-scaling-laws-contrastive-language/) [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/)**Mehdi Cherti, Romain Beaumont, Ross Wightman, et al.*2022 · 769 citations*
29. [**Feature Pyramid Networks for Object Detection arXiv:1612.03144**Tsung-Yi Lin, Piotr Dollar, Ross Girshick, et al.*2016 · 767 citations*](https://md.research.elamur.ai/free/en/papers/lin-2016-feature-pyramid-networks-object-detection/)
30. **[RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers](https://md.research.elamur.ai/free/en/papers/wang-2020-rat-sql-relation-aware-schema/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Bailin Wang, Richard Shin, Xiaodong Liu, et al.*2020 · 753 citations*
31. **[Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation](https://md.research.elamur.ai/free/en/papers/chen-2019-progressive-differentiable-architecture-search-bridging/) [IEEE/CVF International Conference on Computer Vision (ICCV)](https://md.research.elamur.ai/free/en/venues/ieee-cvf-international-conference-on-computer-vision-iccv/)**Xin Chen, Lingxi Xie, Jun Wu, et al.*2019 · 732 citations*
32. **[SGLang: Efficient Execution of Structured Language Model Programs](https://md.research.elamur.ai/free/en/papers/zheng-2023-sglang-efficient-execution-structured-language/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Lianmin Zheng, Liangsheng Yin, Zhiqiang Xie, et al.*2023 · 729 citations*
33. **[FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP](https://md.research.elamur.ai/free/en/papers/akbik-2019-flair-easy-use-framework-state/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Alan Akbik, Tanja Bergmann, Duncan Blythe, et al.*2019 · 648 citations*
34. [**MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework arXiv:2308.00352**Sirui Hong, Mingchen Zhuge, Jiaqi Chen, et al.*2023 · 630 citations*](https://md.research.elamur.ai/free/en/papers/hong-2023-metagpt-meta-programming-multi/)
35. [**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/)
36. [**SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis arXiv:2307.01952**Dustin Podell, Zion English, Kyle Lacey, et al.*2023 · 608 citations*](https://md.research.elamur.ai/free/en/papers/podell-2023-sdxl-improving-latent-diffusion-models/)
37. **[Attention Guided Graph Convolutional Networks for Relation Extraction](https://md.research.elamur.ai/free/en/papers/guo-2019-attention-guided-graph-convolutional-networks/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhijiang Guo, Yan Zhang, Wei Lu*2019 · 602 citations*
38. [**What do you learn from context? Probing for sentence structure in contextualized word representations arXiv:1905.06316**Ian Tenney, Patrick Xia, Berlin Chen, et al.*2019 · 602 citations*](https://md.research.elamur.ai/free/en/papers/tenney-2019-learn-context-probing-sentence-structure/)
39. [**gpt-oss-120b & gpt-oss-20b Model Card arXiv:2508.10925**OpenAI: Sandhini Agarwal, Lama Ahmad, Jason Ai, et al.*2025 · 576 citations*](https://md.research.elamur.ai/free/en/papers/agarwal-2025-gpt-oss-120b-gpt-oss/)
40. [**Training data-efficient image transformers & distillation through attention arXiv:2012.12877**Hugo Touvron, Matthieu Cord, Matthijs Douze, et al.*2020 · 524 citations*](https://md.research.elamur.ai/free/en/papers/touvron-2020-training-data-efficient-image-transformers/)
41. **[Instruction Tuning for Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/zhang-2023-instruction-tuning-large-language-models/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Shengyu Zhang, Linfeng Dong, Xiaoya Li, et al.*2023 · 520 citations*
42. **[DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations](https://md.research.elamur.ai/free/en/papers/giorgi-2021-declutr-deep-contrastive-learning-unsupervised/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**John Giorgi, Osvald Nitski, Bo Wang, et al.*2021 · 516 citations*
43. **[SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking](https://md.research.elamur.ai/free/en/papers/formal-2021-splade-sparse-lexical-expansion/) [International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)](https://md.research.elamur.ai/free/en/venues/international-acm-sigir-conference-on-research-and-development-in-information/)**Thibault Formal, Benjamin Piwowarski, Stéphane Clinchant*2021 · 512 citations*
44. [**Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data Tasks arXiv:1811.01088**Jason Phang, Thibault Févry, Samuel R. Bowman*2018 · 503 citations*](https://md.research.elamur.ai/free/en/papers/phang-2018-sentence-encoders-stilts-supplementary-training/)
45. **[HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training](https://md.research.elamur.ai/free/en/papers/li-2020-hero-hierarchical-encoder-video-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Linjie Li, Yen-Chun Chen, Yu Cheng, et al.*2020 · 481 citations*
46. [**Orca: A Distributed Serving System for Transformer-Based Generative Models OSDI 2022**Gyeong-In Yu, Joo Seong Jeong, Geon-Woo Kim, et al.*2022 · 478 citations*](https://md.research.elamur.ai/free/en/papers/yu-2022-orca-distributed-serving-system-transformer/)
47. **[Are All Languages Created Equal in Multilingual BERT?](https://md.research.elamur.ai/free/en/papers/wu-2020-are-all-languages-created-equal/) [Workshop on Representation Learning for NLP](https://md.research.elamur.ai/free/en/venues/repl4nlp/)**Shijie Wu, Mark Dredze*2020 · 460 citations*
48. **[Neural Document Summarization by Jointly Learning to Score and Select Sentences](https://md.research.elamur.ai/free/en/papers/zhou-2018-neural-document-summarization-jointly-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qingyu Zhou, Nan Yang, Furu Wei, et al.*2018 · 450 citations*
49. [**The Falcon Series of Open Language Models arXiv:2311.16867**Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, et al.*2023 · 443 citations*](https://md.research.elamur.ai/free/en/papers/almazrouei-2023-falcon-series-open-language-models/)
50. [**Kimi k1.5: Scaling Reinforcement Learning with LLMs arXiv:2501.12599**Kimi Team, Angang Du, Bofei Gao, et al.*2025 · 431 citations*](https://md.research.elamur.ai/free/en/papers/kimi-team-2025-kimi-k1-5-scaling-reinforcement/)
51. **[Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis](https://md.research.elamur.ai/free/en/papers/fu-2024-video-mme-first-ever-comprehensive/) [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/)**Chaoyou Fu, Yuhan Dai, Yongdong Luo, et al.*2024 · 427 citations*
52. **[Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling](https://md.research.elamur.ai/free/en/papers/hofstatter-2021-efficiently-teaching-effective-dense-retriever/) [International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)](https://md.research.elamur.ai/free/en/venues/international-acm-sigir-conference-on-research-and-development-in-information/)**Sebastian Hofstatter, Sheng-Chieh Lin, Jheng-Hong Yang, et al.*2021 · 425 citations*
53. [**Tulu 3: Pushing Frontiers in Open Language Model Post-Training arXiv:2411.15124**Nathan Lambert, Jacob Morrison, Valentina Pyatkin, et al.*2024 · 423 citations*](https://md.research.elamur.ai/free/en/papers/lambert-2024-tulu-3-pushing-frontiers-open/)
54. [**The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes arXiv:2005.04790**Douwe Kiela, Hamed Firooz, Aravind Mohan, et al.*2020 · 420 citations*](https://md.research.elamur.ai/free/en/papers/kiela-2020-hateful-memes-challenge-detecting-hate/)
55. [**True Few-Shot Learning with Language Models arXiv:2105.11447**Ethan Perez, Douwe Kiela, Kyunghyun Cho*2021 · 412 citations*](https://md.research.elamur.ai/free/en/papers/perez-2021-true-few-shot-learning-language/)
56. [**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/)
57. [**Editing Models with Task Arithmetic arXiv:2212.04089**Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, et al.*2022 · 402 citations*](https://md.research.elamur.ai/free/en/papers/ilharco-2022-editing-models-task-arithmetic/)
58. **[ViperGPT: Visual Inference via Python Execution for Reasoning](https://md.research.elamur.ai/free/en/papers/suris-2023-vipergpt-visual-inference-python-execution/) [IEEE/CVF International Conference on Computer Vision (ICCV)](https://md.research.elamur.ai/free/en/venues/ieee-cvf-international-conference-on-computer-vision-iccv/)**Didac Suris, Sachit Menon, Carl Vondrick*2023 · 383 citations*
59. **[Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity](https://md.research.elamur.ai/free/en/papers/jeong-2024-adaptive-rag-learning-adapt-retrieval/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Soyeong Jeong, Jinheon Baek, Sukmin Cho, et al.*2024 · 382 citations*
60. **[Reasoning with Latent Structure Refinement for Document-Level Relation Extraction](https://md.research.elamur.ai/free/en/papers/nan-2020-reasoning-latent-structure-refinement-document/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Guoshun Nan, Zhijiang Guo, Ivan Sekulic, et al.*2020 · 382 citations*
61. [**Augmented Language Models: a Survey arXiv:2302.07842**Grégoire Mialon, Roberto Dessì, Maria Lomeli, et al.*2023 · 369 citations*](https://md.research.elamur.ai/free/en/papers/mialon-2023-augmented-language-models-survey/)
62. **[Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds](https://md.research.elamur.ai/free/en/papers/ash-2019-deep-batch-active-learning-diverse/) [International Conference on Learning Representations (ICLR)](https://md.research.elamur.ai/free/en/venues/international-conference-on-learning-representations-iclr/)**Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, et al.*2019 · 362 citations*
63. [**RobBERT: a Dutch RoBERTa-based Language Model arXiv:2001.06286**Pieter Delobelle, Thomas Winters, Bettina Berendt*2020 · 349 citations*](https://md.research.elamur.ai/free/en/papers/delobelle-2020-robbert-dutch-roberta-based-language/)
64. [**PyTorch Distributed: Experiences on Accelerating Data Parallel Training arXiv:2006.15704**Shen Li, Yanli Zhao, Rohan Varma, et al.*2020 · 348 citations*](https://md.research.elamur.ai/free/en/papers/li-2020-pytorch-distributed-experiences-accelerating-data/)
65. **[Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives](https://md.research.elamur.ai/free/en/papers/gao-2023-large-language-models-empowered-agent/) [Humanities and Social Sciences Communications](https://md.research.elamur.ai/free/en/venues/humanities-and-social-sciences-communications/)**Chen Gao, Xiaochong Lan, Nian Li, et al.*2023 · 344 citations*
66. **[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*
67. **[Few-NERD: A Few-shot Named Entity Recognition Dataset](https://md.research.elamur.ai/free/en/papers/ding-2021-few-nerd-few-shot-named/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ning Ding, Guangwei Xu, Yulin Chen, et al.*2021 · 336 citations*
68. **[Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval](https://md.research.elamur.ai/free/en/papers/gao-2022-unsupervised-corpus-aware-language-model/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Luyu Gao, Jamie Callan*2022 · 328 citations*
69. [**Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models arXiv:2506.05176**Yanzhao Zhang, Mingxin Li, Dingkun Long, et al.*2025 · 322 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2025-qwen3-embedding-advancing-text/)
70. [**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/)
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73. **[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*
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77. [**DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines arXiv:2310.03714**Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, et al.*2023 · 285 citations*](https://md.research.elamur.ai/free/en/papers/khattab-2023-dspy-compiling-declarative-language-model/)
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79. **[Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work?](https://md.research.elamur.ai/free/en/papers/pruksachatkun-2020-intermediate-task-transfer-learning-pretrained/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yada Pruksachatkun, Jason Phang, Haokun Liu, et al.*2020 · 281 citations*
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85. [**Wan: Open and Advanced Large-Scale Video Generative Models arXiv:2503.20314**Team Wan, Ang Wang, Baole Ai, et al.*2025 · 260 citations*](https://md.research.elamur.ai/free/en/papers/wan-2025-wan-open-advanced-large-scale/)
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87. [**SocialIQA: Commonsense Reasoning about Social Interactions arXiv:1904.09728**Maarten Sap, Hannah Rashkin, Derek Chen, et al.*2019 · 259 citations*](https://md.research.elamur.ai/free/en/papers/sap-2019-socialiqa-commonsense-reasoning-about-social/)
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90. [**Personality Traits in Large Language Models arXiv:2307.00184**Greg Serapio-Garcia, Mustafa Safdari, Clement Crepy, et al.*2023 · 252 citations*](https://md.research.elamur.ai/free/en/papers/serapio-garcia-2023-personality-traits-large-language-models/)
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