---
title: "Who cites Quoc Viet Le — page 6 — 30,072 papers of our library"
description: "30,072 papers of the Elamur Research library cite the works of Quoc Viet Le, found in their bibliographies · page 6 of 54 · 501–600; the most…"
canonical: "https://research.elamur.ai/free/en/authors/quoc-viet-le/cited-by/page/6/"
---
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# Who cites Quoc Viet Le

47,619 citations of their works from 30,072 papers of our library (of the 149,439 whose bibliographies we read) · 5,372 with a page here, the most cited first · page 6 of 54 · 501–600 · [← Quoc Viet Le](https://md.research.elamur.ai/free/en/authors/quoc-viet-le/)

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501. **[MLS: A Large-Scale Multilingual Dataset for Speech Research](https://md.research.elamur.ai/free/en/papers/pratap-2020-mls-large-scale-multilingual-dataset/) [Interspeech](https://md.research.elamur.ai/free/en/venues/interspeech-usb/)**Vineel Pratap, Qiantong Xu, Anuroop Sriram, et al.*2020 · 523 citations*
502. **[To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks](https://md.research.elamur.ai/free/en/papers/peters-2019-tune-tune-adapting-pretrained-representations/) [Workshop on Representation Learning for NLP](https://md.research.elamur.ai/free/en/venues/repl4nlp/)**Matthew E. Peters, Sebastian Ruder, Noah A. Smith*2019 · 522 citations*
503. **[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*
504. [**OpenAssistant Conversations — Democratizing Large Language Model Alignment arXiv:2304.07327**Andreas Köpf, Yannic Kilcher, Dimitri von Rütte, et al.*2023 · 518 citations*](https://md.research.elamur.ai/free/en/papers/kopf-2023-openassistant-conversations-democratizing-large-language/)
505. **[How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence](https://md.research.elamur.ai/free/en/papers/zhong-2020-how-does-nlp-benefit-legal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haoxi Zhong, Chaojun Xiao, Cunchao Tu, et al.*2020 · 518 citations*
506. [**Deep Learning Scaling is Predictable, Empirically arXiv:1712.00409**Joel Hestness, Sharan Narang, Newsha Ardalani, et al.*2017 · 518 citations*](https://md.research.elamur.ai/free/en/papers/hestness-2017-deep-learning-scaling-predictable-empirically/)
507. [**Baichuan 2: Open Large-scale Language Models arXiv:2309.10305**Aiyuan Yang, Bin Xiao, Bingning Wang, et al.*2023 · 517 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-baichuan-2-open-large-scale/)
508. **[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*
509. **[Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/wang-2016-recursive-neural-conditional-random-fields/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Wenya Wang, Sinno Jialin Pan, Daniel Dahlmeier, et al.*2016 · 516 citations*
510. [**TabFact: A Large-scale Dataset for Table-based Fact Verification arXiv:1909.02164**Wenhu Chen, Hongmin Wang, Jianshu Chen, et al.*2019 · 515 citations*](https://md.research.elamur.ai/free/en/papers/chen-2019-tabfact-large-scale-dataset-table/)
511. [**LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding arXiv:2308.14508**Yushi Bai, Xin Lv, Jiajie Zhang, et al.*2023 · 514 citations*](https://md.research.elamur.ai/free/en/papers/bai-2023-longbench-bilingual-multitask-benchmark/)
512. [**Towards Expert-Level Medical Question Answering with Large Language Models arXiv:2305.09617**Karan Singhal, Tao Tu, Juraj Gottweis, et al.*2023 · 513 citations*](https://md.research.elamur.ai/free/en/papers/singhal-2023-towards-expert-level-medical-question/)
513. **[FinQA: A Dataset of Numerical Reasoning over Financial Data](https://md.research.elamur.ai/free/en/papers/chen-2021-finqa-dataset-numerical-reasoning-over/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhiyu Chen, Wenhu Chen, Charese Smiley, et al.*2021 · 513 citations*
514. [**Learned in Translation: Contextualized Word Vectors arXiv:1708.00107**Bryan McCann, James Bradbury, Caiming Xiong, et al.*2017 · 512 citations*](https://md.research.elamur.ai/free/en/papers/mccann-2017-learned-translation-contextualized-word-vectors/)
515. [**Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time arXiv:2203.05482**Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre, et al.*2022 · 507 citations*](https://md.research.elamur.ai/free/en/papers/wortsman-2022-model-soups-averaging-weights-multiple/)
516. **[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 · 506 citations*
517. **[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 · 506 citations*
518. **[FLAT: Chinese NER Using Flat-Lattice Transformer](https://md.research.elamur.ai/free/en/papers/li-2020-flat-chinese-ner-flat-lattice/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaonan Li, Hang Yan, Xipeng Qiu, et al.*2020 · 505 citations*
519. **[Does String-Based Neural MT Learn Source Syntax?](https://md.research.elamur.ai/free/en/papers/shi-2016-does-string-based-neural-mt/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xing Shi, Inkit Padhi, Kevin Knight*2016 · 504 citations*
520. **[Two/Too Simple Adaptations of Word2Vec for Syntax Problems](https://md.research.elamur.ai/free/en/papers/ling-2015-two-too-simple-adaptations-word2vec/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Wang Ling, Chris Dyer, Alan W. Black, et al.*2015 · 504 citations*
521. **[Sparse, Dense, and Attentional Representations for Text Retrieval](https://md.research.elamur.ai/free/en/papers/luan-2021-sparse-dense-attentional-representations-text/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Yi Luan, Jacob Eisenstein, Kristina Toutanova, et al.*2021 · 503 citations*
522. [**Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model arXiv:2401.09417**Lianghui Zhu, Bencheng Liao, Qian Zhang, et al.*2024 · 500 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2024-vision-mamba-efficient-visual-representation/)
523. [**Few-shot Learning with Multilingual Language Models arXiv:2112.10668**Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, et al.*2021 · 500 citations*](https://md.research.elamur.ai/free/en/papers/lin-2021-few-shot-learning-multilingual-language/)
524. **[Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses](https://md.research.elamur.ai/free/en/papers/lowe-2017-towards-automatic-turing-test-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ryan Lowe, Michael Noseworthy, Iulian V. Serban, et al.*2017 · 499 citations*
525. **[Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations](https://md.research.elamur.ai/free/en/papers/wang-2024-math-shepherd-verify-reinforce-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Peiyi Wang, Lei Li, Zhihong Shao, et al.*2024 · 497 citations*
526. **[Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents](https://md.research.elamur.ai/free/en/papers/sun-2023-chatgpt-good-search-investigating-large/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Weiwei Sun, Lingyong Yan, Xinyu Ma, et al.*2023 · 496 citations*
527. [**GLaM: Efficient Scaling of Language Models with Mixture-of-Experts arXiv:2112.06905**Nan Du, Yanping Huang, Andrew M. Dai, et al.*2021 · 496 citations*](https://md.research.elamur.ai/free/en/papers/du-2021-glam-efficient-scaling-language-models/)
528. [**Regularizing Neural Networks by Penalizing Confident Output Distributions arXiv:1701.06548**Gabriel Pereyra, George Tucker, Jan Chorowski, et al.*2017 · 496 citations*](https://md.research.elamur.ai/free/en/papers/pereyra-2017-regularizing-neural-networks-penalizing-confident/)
529. [**Language Models are Multilingual Chain-of-Thought Reasoners arXiv:2210.03057**Freda Shi, Mirac Suzgun, Markus Freitag, et al.*2022 · 494 citations*](https://md.research.elamur.ai/free/en/papers/shi-2022-language-models-multilingual-chain-thought/)
530. **[Semi-Supervised Sequence Modeling with Cross-View Training](https://md.research.elamur.ai/free/en/papers/clark-2018-semi-supervised-sequence-modeling-cross/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Kevin Clark, Minh-Thang Luong, Christopher D. Manning, et al.*2018 · 494 citations*
531. **[Inference-Time Intervention: Eliciting Truthful Answers from a Language Model](https://md.research.elamur.ai/free/en/papers/li-2023-inference-time-intervention-eliciting-truthful/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Kenneth Li, Oam Patel, Fernanda Viegas, et al.*2023 · 492 citations*
532. [**REPLUG: Retrieval-Augmented Black-Box Language Models arXiv:2301.12652**Weijia Shi, Sewon Min, Michihiro Yasunaga, et al.*2023 · 492 citations*](https://md.research.elamur.ai/free/en/papers/shi-2023-replug-retrieval-augmented-black-box/)
533. **[A Convolutional Encoder Model for Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/gehring-2017-convolutional-encoder-model-neural-machine/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jonas Gehring, Michael Auli, David Grangier, et al.*2017 · 492 citations*
534. [**Rethinking Attention with Performers arXiv:2009.14794**Krzysztof Choromanski, Valerii Likhosherstov, David Dohan, et al.*2020 · 491 citations*](https://md.research.elamur.ai/free/en/papers/choromanski-2020-rethinking-attention-performers/)
535. [**DyNet: The Dynamic Neural Network Toolkit arXiv:1701.03980**Graham Neubig, Chris Dyer, Yoav Goldberg, et al.*2017 · 489 citations*](https://md.research.elamur.ai/free/en/papers/neubig-2017-dynet-dynamic-neural-network-toolkit/)
536. **[DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference](https://md.research.elamur.ai/free/en/papers/xin-2020-deebert-dynamic-early-exiting-accelerating/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ji Xin, Raphael Tang, Jaejun Lee, et al.*2020 · 487 citations*
537. **[EmoNet: Fine-Grained Emotion Detection with Gated Recurrent Neural Networks](https://md.research.elamur.ai/free/en/papers/mageed-2017-emonet-fine-grained-emotion-detection/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Muhammad Abdul-Mageed, Lyle Ungar*2017 · 487 citations*
538. **[What do Neural Machine Translation Models Learn about Morphology?](https://md.research.elamur.ai/free/en/papers/belinkov-2017-what-do-neural-machine-translation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yonatan Belinkov, Nadir Durrani, Fahim Dalvi, et al.*2017 · 487 citations*
539. **[ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction](https://md.research.elamur.ai/free/en/papers/santhanam-2022-colbertv2-effective-efficient-retrieval-lightweight/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Keshav Santhanam, Omar Khattab, Jon Saad-Falcon, et al.*2022 · 485 citations*
540. [**Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned arXiv:2209.07858**Deep Ganguli, Liane Lovitt, Jackson Kernion, et al.*2022 · 485 citations*](https://md.research.elamur.ai/free/en/papers/ganguli-2022-red-teaming-language-models-reduce/)
541. **[ToTTo: A Controlled Table-To-Text Generation Dataset](https://md.research.elamur.ai/free/en/papers/parikh-2020-totto-controlled-table-text-generation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ankur Parikh, Xuezhi Wang, Sebastian Gehrmann, et al.*2020 · 482 citations*
542. [**SimVLM: Simple Visual Language Model Pretraining with Weak Supervision arXiv:2108.10904**Zirui Wang, Jiahui Yu, Adams Wei Yu, et al.*2021 · 481 citations*](https://md.research.elamur.ai/free/en/papers/wang-2021-simvlm-simple-visual-language-model/)
543. **[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*
544. **[Legal Judgment Prediction via Topological Learning](https://md.research.elamur.ai/free/en/papers/zhong-2018-legal-judgment-prediction-topological-learning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Haoxi Zhong, Zhipeng Guo, Cunchao Tu, et al.*2018 · 480 citations*
545. **[Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion](https://md.research.elamur.ai/free/en/papers/joulin-2018-loss-translation-learning-bilingual-word/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Armand Joulin, Piotr Bojanowski, Tomas Mikolov, et al.*2018 · 479 citations*
546. [**ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware arXiv:1812.00332**Han Cai, Ligeng Zhu, Song Han*2018 · 479 citations*](https://md.research.elamur.ai/free/en/papers/cai-2018-proxylessnas-direct-neural-architecture-search/)
547. [**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/)
548. [**ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic arXiv:2101.01785 · ACL-2021 camera ready version**Muhammad Abdul-Mageed, AbdelRahim Elmadany, El Moatez Billah Nagoudi*2021 · 478 citations*](https://md.research.elamur.ai/free/en/papers/abdul-mageed-2021-arbert-marbert-deep-bidirectional-transformers/)
549. [**LLaVA-OneVision: Easy Visual Task Transfer arXiv:2408.03326**Bo Li, Yuanhan Zhang, Dong Guo, et al.*2024 · 477 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-llava-onevision-easy-visual-task/)
550. **[An Unsupervised Neural Attention Model for Aspect Extraction](https://md.research.elamur.ai/free/en/papers/he-2017-unsupervised-neural-attention-model-aspect/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ruidan He, Wee Sun Lee, Hwee Tou Ng, et al.*2017 · 477 citations*
551. [**In-context Learning and Induction Heads arXiv:2209.11895**Catherine Olsson, Nelson Elhage, Neel Nanda, et al.*2022 · 475 citations*](https://md.research.elamur.ai/free/en/papers/olsson-2022-context-learning-induction-heads/)
552. **[Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models](https://md.research.elamur.ai/free/en/papers/luong-2016-achieving-open-vocabulary-neural-machine/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Minh-Thang Luong, Christopher D. Manning*2016 · 475 citations*
553. [**Data Augmentation using Pre-trained Transformer Models arXiv:2003.02245**Varun Kumar, Ashutosh Choudhary, Eunah Cho*2020 · 474 citations*](https://md.research.elamur.ai/free/en/papers/kumar-2020-data-augmentation-using-pre-trained/)
554. **[When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation?](https://md.research.elamur.ai/free/en/papers/qi-2018-when-why-are-pre-trained/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Ye Qi, Devendra Sachan, Matthieu Felix, et al.*2018 · 474 citations*
555. [**Learning to Generate Reviews and Discovering Sentiment arXiv:1704.01444**Alec Radford, Rafal Jozefowicz, Ilya Sutskever*2017 · 474 citations*](https://md.research.elamur.ai/free/en/papers/radford-2017-learning-generate-reviews-discovering-sentiment/)
556. [**Show your work: Scratchpads for intermediate computation with language models***2021 · 473 citations*](https://md.research.elamur.ai/free/en/papers/al-2021-show-your-work-scratchpads-intermediate/)
557. [**Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection arXiv:2303.05499**Shilong Liu, Zhaoyang Zeng, Tianhe Ren, et al.*2023 · 472 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-grounding-dino-marrying-dino-grounded/)
558. [**Large Language Models: A Survey arXiv:2402.06196**Shervin Minaee, Tomas Mikolov, Narjes Nikzad, et al.*2024 · 471 citations*](https://md.research.elamur.ai/free/en/papers/minaee-2024-large-language-models-survey/)
559. **[PPT: Pre-trained Prompt Tuning for Few-shot Learning](https://md.research.elamur.ai/free/en/papers/gu-2022-ppt-pre-trained-prompt-tuning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuxian Gu, Xu Han, Zhiyuan Liu, et al.*2022 · 471 citations*
560. [**Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT arXiv:1904.09077**Shijie Wu, Mark Dredze*2019 · 471 citations*](https://md.research.elamur.ai/free/en/papers/wu-2019-beto-bentz-becas-surprising-cross/)
561. **[Representation Learning Using Multi-Task Deep Neural Networks for Semantic Classification and Information Retrieval](https://md.research.elamur.ai/free/en/papers/liu-2015-representation-learning-multi-task-deep/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Xiaodong Liu, Jianfeng Gao, Xiaodong He, et al.*2015 · 471 citations*
562. [**P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks arXiv:2110.07602**Xiao Liu, Kaixuan Ji, Yicheng Fu, et al.*2021 · 469 citations*](https://md.research.elamur.ai/free/en/papers/liu-2021-p-tuning-v2-prompt-tuning/)
563. [**MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts arXiv:2310.02255**Pan Lu, Hritik Bansal, Tony Xia, et al.*2023 · 468 citations*](https://md.research.elamur.ai/free/en/papers/lu-2023-mathvista-evaluating-mathematical-reasoning-foundation/)
564. **[Hierarchical Transformers for Multi-Document Summarization](https://md.research.elamur.ai/free/en/papers/liu-2019-hierarchical-transformers-multi-document-summarization/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yang Liu, Mirella Lapata*2019 · 468 citations*
565. [**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/)
566. [**Action-Conditional Video Prediction using Deep Networks in Atari Games arXiv:1507.08750**Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, et al.*2015 · 467 citations*](https://md.research.elamur.ai/free/en/papers/oh-2015-action-conditional-video-prediction-using/)
567. **[End-to-end learning of semantic role labeling using recurrent neural networks](https://md.research.elamur.ai/free/en/papers/zhou-2015-end-end-learning-semantic-role/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jie Zhou, Wei Xu*2015 · 466 citations*
568. **[CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X](https://md.research.elamur.ai/free/en/papers/zheng-2023-codegeex-pre-trained-model-code/) [ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)](https://md.research.elamur.ai/free/en/venues/acm-sigkdd-conference-on-knowledge-discovery-and-data-mining-kdd/)**Qinkai Zheng, Xiao Xia, Xu Zou, et al.*2023 · 465 citations*
569. **[Joint Embedding of Words and Labels for Text Classification](https://md.research.elamur.ai/free/en/papers/wang-2018-joint-embedding-words-labels-text/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Guoyin Wang, Chunyuan Li, Wenlin Wang, et al.*2018 · 465 citations*
570. **[MVBench: A Comprehensive Multi-modal Video Understanding Benchmark](https://md.research.elamur.ai/free/en/papers/li-2023-mvbench-comprehensive-multi-modal-video/) [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/)**Kunchang Li, Yali Wang, Yinan He, et al.*2023 · 463 citations*
571. **[Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation](https://md.research.elamur.ai/free/en/papers/gao-2023-text-sql-empowered-large-language/) [Proceedings of the VLDB Endowment](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-vldb-endowment/)**Dawei Gao, Haibin Wang, Yaliang Li, et al.*2023 · 463 citations*
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573. [**A Persona-Based Neural Conversation Model arXiv:1603.06155**Jiwei Li, Michel Galley, Chris Brockett, et al.*2016 · 462 citations*](https://md.research.elamur.ai/free/en/papers/li-2016-persona-based-neural-conversation-model/)
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578. [**Ethical and social risks of harm from Language Models arXiv:2112.04359**Laura Weidinger, John Mellor, Maribeth Rauh, et al.*2021 · 456 citations*](https://md.research.elamur.ai/free/en/papers/weidinger-2021-ethical-social-risks-harm-language/)
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580. **[Bilingual Word Representations with Monolingual Quality in Mind](https://md.research.elamur.ai/free/en/papers/luong-2015-bilingual-word-representations-monolingual-quality/) [Workshop on Vector Space Modeling for Natural Language Processing](https://md.research.elamur.ai/free/en/venues/workshop-on-vector-space-modeling-for-natural-language-processing/)**Thang Luong, Hieu Pham, Christopher D. Manning*2015 · 456 citations*
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589. [**FreeLB: Enhanced Adversarial Training for Natural Language Understanding arXiv:1909.11764**Chen Zhu, Yu Cheng, Zhe Gan, et al.*2019 · 452 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2019-freelb-enhanced-adversarial-training-natural/)
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594. [**In-Datacenter Performance Analysis of a Tensor Processing Unit arXiv:1704.04760**Norman P. Jouppi; Cliff Young; Nishant Patil; David Patterson; Gaurav Agrawal; Raminder Bajwa; Sarah Bates; Suresh Bhatia; Nan Boden; Al Borchers; Rick Boyle; Pierre-luc Cantin; Clifford Chao; Chris Clark; Jeremy Coriell; Mike Daley; Matt Dau; Jeffrey Dean; Ben Gelb; Tara Vazir Ghaemmaghami; Rajendra Gottipati; William Gulland; Robert Hagmann; C. Richard Ho; Doug Hogberg; John Hu; Robert Hundt; Dan Hurt; Julian Ibarz; Aaron Jaffey; Alek Jaworski; Alexander Kaplan; Harshit Khaitan; Andy Koch; Naveen Kumar; Steve Lacy; James Laudon; James Law; Diemthu Le; Chris Leary; Zhuyuan Liu; Kyle Lucke; Alan Lundin; Gordon MacKean; Adriana Maggiore; Maire Mahony; Kieran Miller; Rahul Nagarajan; Ravi Narayanaswami; Ray Ni; Kathy Nix; Thomas Norrie; Mark Omernick; Narayana Penukonda; Andy Phelps; Jonathan Ross; Matt Ross; Amir Salek; Emad Samadiani; Chris Severn; Gregory Sizikov; Matthew Snelham; Jed Souter; Dan Steinberg; Andy Swing; Mercedes Tan; Gregory Thorson; Bo Tian; Horia Toma; Erick Tuttle; Vijay Vasudevan; Richard Walter; Walter Wang; Eric Wilcox;, Doe Hyun Yoon*2017 · 449 citations*](https://md.research.elamur.ai/free/en/papers/jouppi-2017-datacenter-performance-analysis-tensor-processing/)
595. **[Masked Language Model Scoring](https://md.research.elamur.ai/free/en/papers/salazar-2020-masked-language-model-scoring/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Julian Salazar, Davis Liang, Toan Q. Nguyen, et al.*2020 · 447 citations*
596. [**FinGPT: Open-Source Financial Large Language Models arXiv:2306.06031**Hongyang Yang, Xiao-Yang Liu, Christina Dan Wang*2023 · 445 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-fingpt-open-source-financial-large/)
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598. [**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/)
599. **[oLMpics — On what Language Model Pre-training Captures](https://md.research.elamur.ai/free/en/papers/talmor-2019-olmpics-language-model-pre-training/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Alon Talmor, Yanai Elazar, Yoav Goldberg, et al.*2019 · 440 citations*
600. **[Deep Communicating Agents for Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/celikyilmaz-2018-deep-communicating-agents-abstractive-summarization/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Asli Celikyilmaz, Antoine Bosselut, Xiaodong He, et al.*2018 · 440 citations*

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