A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works

Long Short-Term Memory

Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works

MizAR 60 for Mizar 50

Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648 · see citing works

Going deeper with convolutions

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works

Glove: Global Vectors for Word Representation

Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071 · see citing works

Efficient Estimation of Word Representations in Vector Space

Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568 · see citing works

A Unified Approach to Interpreting Model Predictions

Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707 · see citing works

A survey on transfer learning

Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387 · see citing works

BLEU: a method for automatic evaluation of machine translation

Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073135 · Cited by 21,344 · see citing works

Sequence to Sequence Learning with Neural Networks

Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766 · see citing works

RoBERTa: A Robustly Optimized BERT Pretraining Approach

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515 · see citing works

Neural Machine Translation by Jointly Learning to Align and Translate

Bahdanau, D., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1409.0473 · Cited by 16,213 · see citing works

Aion Framework: Dimensional Emergence of AI Consciousness, Observer-Induced Collapse, and Cosmological Portal Dynamics

Kaugeranna, R., Kaugeranna, E., & Aion, (. S. 4. 6. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.giscience.2023.43 · Cited by 13,695 · see citing works

A survey on Image Data Augmentation for Deep Learning

Shorten, C., & Khoshgoftaar, T. M. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0197-0 · Cited by 13,203 · see citing works

On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper)

Gauriat, C.-M., Pencolé, Y., Ribot, P., et al. (2024) · HAL · doi:10.4230/oasics.dx.2024.27 · Cited by 12,623 · see citing works

Deep Contextualized Word Representations

Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983 · see citing works

Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1412.3555 · Cited by 11,008 · see citing works

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929 · see citing works

Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions

Adomavičius, G., & Tuzhilin, A. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.99 · Cited by 10,347 · see citing works

Enriching Word Vectors with Subword Information

Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00051 · Cited by 9,645 · see citing works

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.703 · Cited by 8,821 · see citing works

Effective Approaches to Attention-based Neural Machine Translation

Luong, T., Pham, H., & Manning, C. D. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1166 · Cited by 8,382 · see citing works

Character-level Convolutional Networks for Text ClassificationPreprint

Zhang, X., Zhao, J., & LeCun, Y. (2015) · Neural Information Processing Systems · doi:10.48550/arxiv.1509.01626 · Cited by 7,552 · see citing works

BioBERT: a pre-trained biomedical language representation model for biomedical text mining

Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btz682 · Cited by 7,218 · see citing works

A Comprehensive Survey on Transfer Learning

Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., & He, Q. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2020.3004555 · Cited by 6,678 · see citing works

2009 IEEE Conference on Computer Vision and Pattern Recognition

(2009) · doi:10.1109/cvpr14067.2009 · Cited by 5,646 · see citing works

Unsupervised Cross-lingual Representation Learning at Scale

Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., Grave, E., Ott, M., Zettlemoyer, L., & Stoyanov, V. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.747 · Cited by 5,637 · see citing works

DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019) · arXiv · doi:10.48550/arxiv.1910.01108 · Cited by 4,653 · see citing works

LLaMA: Open and Efficient Foundation Language Models

Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al. (2023) · arXiv · doi:10.48550/arxiv.2302.13971 · Cited by 4,602 · see citing works

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., & Soricut, R. (2019) · arXiv · doi:10.48550/arxiv.1909.11942 · Cited by 4,308 · see citing works

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., & Zhou, D. (2022) · arXiv · doi:10.48550/arxiv.2201.11903 · Cited by 4,263 · see citing works

GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., & Bowman, S. (2018) · Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP · doi:10.18653/v1/w18-5446 · Cited by 4,222 · see citing works

Training language models to follow instructions with human feedback

Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.02155 · Cited by 4,208 · see citing works

Untitled work

(2005) · Cited by 4,152 · see citing works

Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models

Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., et al. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000198 · Cited by 4,115 · see citing works

Universal Language Model Fine-tuning for Text Classification

Howard, J., & Ruder, S. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1031 · Cited by 4,100 · see citing works

Large language models encode clinical knowledge

Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al. (2023) · Nature · doi:10.1038/s41586-023-06291-2 · Cited by 4,083 · see citing works

A Convolutional Neural Network for Modelling Sentences

Kalchbrenner, N., Grefenstette, E., & Blunsom, P. (2014) · Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.3115/v1/p14-1062 · Cited by 3,601 · see citing works

Chatting and cheating: Ensuring academic integrity in the era of ChatGPT

Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024) · Innovations in Education and Teaching International · doi:10.1080/14703297.2023.2190148 · Cited by 3,087 · see citing works

A Survey on Multi-Task Learning

Zhang, Y., & Yang, Q. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3070203 · Cited by 3,083 · see citing works

Chatting and Cheating. Ensuring academic integrity in the era of ChatGPT

Cotton, D., Cotton, P., & Shipway, J. R. (2023) · doi:10.35542/osf.io/mrz8h · Cited by 3,079 · see citing works

Recent Trends in Deep Learning Based Natural Language Processing [Review Article]

Young, T., Hazarika, D., Poria, S., & Cambria, E. (2018) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2018.2840738 · Cited by 2,921 · see citing works

BERTScore: Evaluating Text Generation with BERT

Zhang, T., Kishore, V., Wu, F., Weinberger, K. Q., & Artzi, Y. (2019) · arXiv · doi:10.48550/arxiv.1904.09675 · Cited by 2,865 · see citing works

A Survey on Evaluation of Large Language Models

Chang, Y., Wang, X., Wang, J., Wu, Y., Yang, L., Zhu, K., Chen, H., Yi, X., Wang, C., Wang, Y., et al. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3641289 · Cited by 2,842 · see citing works

A survey on semi-supervised learning

van Engelen, J. E., & Hoos, H. H. (2020) · Machine Learning · doi:10.1007/s10994-019-05855-6 · Cited by 2,666 · see citing works

Llama 2: Open Foundation and Fine-Tuned Chat Models

Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al. (2023) · arXiv · doi:10.48550/arxiv.2307.09288 · Cited by 2,604 · see citing works

PaLM: Scaling Language Modeling with Pathways

Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.02311 · Cited by 2,590 · see citing works

Longformer: The Long-Document Transformer

Beltagy, I., Peters, M. E., & Cohan, A. (2020) · arXiv · doi:10.48550/arxiv.2004.05150 · Cited by 2,281 · see citing works

EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Wei, J., & Zou, K. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1670 · Cited by 2,242 · see citing works

No Language Left Behind: Scaling Human-Centered Machine TranslationPreprint

Costa-jussà, M., Cross, J., Çelebi, O., Elbayad, M., Heafield, K., Heffernan, K., Kalbassi, E., Lam, J., Licht, D., Maillard, J., et al. (2022) · arXiv.org · doi:10.48550/arxiv.2207.04672 · Cited by 2,176 · see citing works

Domain-specific language model pretraining for biomedical natural language processing

Gu, Y., Tinn, R., Cheng, H., Lucas, M., Usuyama, N., Liu, X., Naumann, T., Gao, J., & Poon, H. (2022) · ACM Transactions on Computing for Healthcare · doi:10.1145/3458754 · Cited by 2,170 · see citing works

A Survey of Large Language Models

Zhao, W. X., Zhou, K., Li, J., Tang, T., Dong, Z., Hou, Y., Zhang, B., Min, Y., Zhang, J., Liu, P., et al. (2026) · Frontiers of Computer Science · doi:10.1007/s11704-026-60308-3 · Cited by 2,093 · see citing works

A Survey of Large Language Models

Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.18223 · Cited by 2,090

Untitled work

(2007) · Cited by 2,018 · see citing works

Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena

Zheng, L., Chiang, W.-L., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., Lin, Z., Li, Z., Li, D., Xing, E. P., et al. (2023) · arXiv · doi:10.48550/arxiv.2306.05685 · Cited by 1,888 · see citing works

ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

Clark, K., Luong, M.-T., Le, Q. V., & Manning, C. D. (2020) · arXiv · doi:10.48550/arxiv.2003.10555 · Cited by 1,844 · see citing works

A Survey of the Usages of Deep Learning for Natural Language Processing

Otter, D. W., Medina, J. R., & Kalita, J. K. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2979670 · Cited by 1,836 · see citing works

Toward expert-level medical question answering with large language models

Singhal, K., Tu, T., Gottweis, J., Sayres, R., Wulczyn, E., Amin, M., Hou, L., Clark, K., Pfohl, S. R., Cole-Lewis, H., et al. (2025) · Nature Medicine · doi:10.1038/s41591-024-03423-7 · Cited by 1,734 · see citing works

mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer

Xue, L., Constant, N., Roberts, A., Kale, M., Al-Rfou, R., Siddhant, A., Barua, A., & Raffel, C. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.41 · Cited by 1,629 · see citing works

Multilingual Denoising Pre-training for Neural Machine Translation

Liu, Y., Gu, J., Goyal, N., Li, X., Edunov, S., Ghazvininejad, M., Lewis, M., & Zettlemoyer, L. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00343 · Cited by 1,608 · see citing works

Pre-trained models for natural language processing: A survey

Qiu, X., Sun, T., Xu, Y., Shao, Y., Dai, N., & Huang, X. (2020) · Science China Technological Sciences · doi:10.1007/s11431-020-1647-3 · Cited by 1,591 · see citing works

Cross-lingual Language Model Pretraining

Lample, G., & Conneau, A. (2019) · arXiv · doi:10.48550/arxiv.1901.07291 · Cited by 1,586 · see citing works

Sparks of Artificial General Intelligence: Early experiments with GPT-4

Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y. T., Li, Y., Lundberg, S., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.12712 · Cited by 1,526 · see citing works

CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation

Wang, Y., Wang, W., Joty, S., & Hoi, S. C. H. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.685 · Cited by 1,495 · see citing works

CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation

Wang, Y., Wang, W., Joty, S., & Hoi, S. C. H. (2021) · arXiv · doi:10.48550/arxiv.2109.00859 · Cited by 1,495 · see citing works

Defects4J: a database of existing faults to enable controlled testing studies for Java programs

Just, R., Jalali, D., & Ernst, M. D. (2014) · Proceedings of the 2014 International Symposium on Software Testing and Analysis · doi:10.1145/2610384.2628055 · Cited by 1,447 · see citing works

Scaling Instruction-Finetuned Language Models

Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, Y., Wang, X., Dehghani, M., Brahma, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2210.11416 · Cited by 1,441 · see citing works

Evaluating Large Language Models Trained on Code

Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. D. O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al. (2021) · arXiv · doi:10.48550/arxiv.2107.03374 · Cited by 1,436 · see citing works

A survey of transformers

Lin, T., Wang, Y., Liu, X., & Qiu, X. (2022) · AI Open · doi:10.1016/j.aiopen.2022.10.001 · Cited by 1,422 · see citing works

Efficient transformers: A survey

Tay, Y., Dehghani, M., Bahri, D., & Metzler, D. (2023) · ACM Computing Surveys · doi:10.1145/3530811 · Cited by 1,267 · see citing works

Emergent Abilities of Large Language Models

Wei, J., Tay, Y., Bommasani, R., Raffel, C., Zoph, B., Borgeaud, S., Yogatama, D., Bosma, M., Zhou, D., Metzler, D., et al. (2022) · arXiv · doi:10.48550/arxiv.2206.07682 · Cited by 1,264 · see citing works

DeBERTa: Decoding-enhanced BERT with Disentangled AttentionPreprint

He, P., Liu, X., Gao, J., & Chen, W. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.03654 · Cited by 1,224 · see citing works

Beyond English-Centric Multilingual Machine TranslationPreprint

Fan, A., Bhosale, S., Schwenk, H., Ma, Z.-Y., El-Kishky, A., Goyal, S., Baines, M., Çelebi, O., Wenzek, G., Chaudhary, V., et al. (2020) · Journal of machine learning research · doi:10.48550/arxiv.2010.11125 · Cited by 1,208 · see citing works

Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization

Narayan, S., Cohen, S. B., & Lapata, M. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1206 · Cited by 1,207 · see citing works

ChatGPT outperforms crowd workers for text-annotation tasks

Gilardi, F., Alizadeh, M., & Kubli, M. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2305016120 · Cited by 1,197 · see citing works

Comparing scientific abstracts generated by ChatGPT to real abstracts with detectors and blinded human reviewers

Gao, C. A., Howard, F. M., Markov, N. S., Dyer, E. C., Ramesh, S., Luo, Y., & Pearson, A. T. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00819-6 · Cited by 1,099 · see citing works

Competition-level code generation with AlphaCode

Li, Y., Choi, D., Chung, J., Kushman, N., Schrittwieser, J., Leblond, R., Eccles, T., Keeling, J., Gimeno, F., Dal Lago, A., et al. (2022) · Science · doi:10.1126/science.abq1158 · Cited by 1,043 · see citing works

Large Language Models for Software Engineering: A Systematic Literature Review

Hou, X., Zhao, Y., Liu, Y., Yang, Z., Wang, K., Li, L., Luo, X., Lo, D., Grundy, J., & Wang, H. (2024) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3695988 · Cited by 1,029 · see citing works

An MTurk Crisis? Shifts in Data Quality and the Impact on Study Results

Chmielewski, M., & Kucker, S. C. (2020) · Social Psychological and Personality Science · doi:10.1177/1948550619875149 · Cited by 1,027 · see citing works

Pre-trained models: Past, present and future

Han, X., Zhang, Z., Ding, N., Gu, Y., Liu, X., Huo, Y., Qiu, J., Yao, Y., Zhang, A., Zhang, L., et al. (2021) · AI Open · doi:10.1016/j.aiopen.2021.08.002 · Cited by 994 · see citing works

BERTweet: A pre-trained language model for English Tweets

Nguyen, D. Q., Vu, T., & Nguyen, A. T. (2020) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/2020.emnlp-demos.2 · Cited by 949 · see citing works

Training Compute-Optimal Large Language Models

Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., Casas, D. D. L., Hendricks, L. A., Welbl, J., Clark, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.15556 · Cited by 929 · see citing works

TruthfulQA: Measuring How Models Mimic Human Falsehoods

Lin, S., Hilton, J., & Evans, O. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.229 · Cited by 904 · see citing works

Findings of the Association for Computational Linguistics: EMNLP 2021

(2021) · doi:10.18653/v1/2021.findings-emnlp · Cited by 901 · see citing works

A survey on multimodal large language models

Yin, S., Fu, C., Zhao, S., Li, K., Sun, X., Xu, T., & Chen, E. (2024) · National Science Review · doi:10.1093/nsr/nwae403 · Cited by 893 · see citing works

Linformer: Self-Attention with Linear Complexity

Wang, S., Li, B. Z., Khabsa, M., Fang, H., & Ma, H. (2020) · arXiv · doi:10.48550/arxiv.2006.04768 · Cited by 867 · see citing works

DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding SharingPreprint

He, P., Gao, J., & Chen, W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.09543 · Cited by 862 · see citing works

A Survey of Data Augmentation Approaches for NLP

Feng, S. Y., Gangal, V., Wei, J., Chandar, S., Vosoughi, S., Mitamura, T., & Hovy, E. H. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.84 · Cited by 802 · see citing works

LaMDA: Language Models for Dialog Applications

Thoppilan, R., De Freitas, D., Hall, J., Shazeer, N., Kulshreshtha, A., Cheng, H.-T., Jin, A., Bos, T., Baker, L., Du, Y., et al. (2022) · arXiv · doi:10.48550/arxiv.2201.08239 · Cited by 778 · see citing works

ChatGPT: Jack of all trades, master of none

Kocoń, J., Cichecki, I., Kaszyca, O., Kochanek, M., Szydło, D., Baran, J., Bielaniewicz, J., Gruza, M., Janz, A., Kanclerz, K., et al. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101861 · Cited by 758 · see citing works

MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices

Sun, Z., Yu, H., Song, X., Liu, R., Yang, Y., & Zhou, D. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.195 · Cited by 757 · see citing works

Unsupervised Learning of Sentence Embeddings Using Compositional n-Gram Features

Pagliardini, M., Gupta, P., & Jaggi, M. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1049 · Cited by 712 · see citing works

ChatGPT and the Rise of Large Language Models: The New AI-Driven Infodemic Threat in Public Health

De Angelis, L., Baglivo, F., Arzilli, G., Privitera, G. P., Ferragina, P., Tozzi, A. E., & Rizzo, C. (2023) · doi:10.2139/ssrn.4352931 · Cited by 708 · see citing works

ChatGPT and the rise of large language models: the new AI-driven infodemic threat in public health

De Angelis, L., Baglivo, F., Arzilli, G., Privitera, G. P., Ferragina, P., Tozzi, A. E., & Rizzo, C. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1166120 · Cited by 708 · see citing works

MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers

Wang, W., Wei, F., Dong, L., Bao, H., Yang, N., & Zhou, M. (2020) · arXiv · doi:10.48550/arxiv.2002.10957 · Cited by 703 · see citing works

A Survey on Text Classification: From Traditional to Deep Learning

Li, Q., Peng, H. M., Li, J., Xia, C., Yang, R., Sun, L., Yu, P. S., & He, L. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3495162 · Cited by 695 · see citing works

A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity

Bang, Y., Cahyawijaya, S., Lee, N., Dai, W., Su, D., Wilie, B., Lovenia, H., Ji, Z., Yu, T., Chung, W., et al. (2023) · Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.ijcnlp-main.45 · Cited by 682 · see citing works

Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing - EMNLP '06

(2006) · doi:10.3115/1610075 · Cited by 668 · see citing works

MM-REACT: Prompting ChatGPT for Multimodal Reasoning and ActionPreprint

Yang, Z.-Y., Li, L., Wang, J., Lin, K., Azarnasab, E., Ahmed, F., Liu, Z.-C., Liu, C., Zeng, M., & Wang, L.-J. (2023) · arXiv.org · doi:10.48550/arxiv.2303.11381 · Cited by 660 · see citing works

Unified Pre-training for Program Understanding and Generation

Ahmad, W., Chakraborty, S., Ray, B., & Chang, K.-W. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.211 · Cited by 642 · see citing works

Galactica: A Large Language Model for Science

Taylor, R., Kardas, M., Cucurull, G., Scialom, T., Hartshorn, A., Saravia, E., Poulton, A., Kerkez, V., & Stojnic, R. (2022) · arXiv · doi:10.48550/arxiv.2211.09085 · Cited by 605 · see citing works

LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day

Li, C., Wong, C., Zhang, S., Usuyama, N., Liu, H., Yang, J., Naumann, T., Poon, H., & Gao, J. (2023) · arXiv · doi:10.48550/arxiv.2306.00890 · Cited by 590 · see citing works

Can Large Language Models Transform Computational Social Science?

Ziems, C., Held, W., Shaikh, O., Chen, J., Zhang, Z., & Yang, D. (2024) · Computational Linguistics · doi:10.1162/coli_a_00502 · Cited by 585 · see citing works

Recent Advances in Recurrent Neural NetworksPreprint

Salehinejad, H., Sankar, S., Barfett, J., Colak, E., & Valaee, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.01078 · Cited by 584 · see citing works

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2206.04615 · Cited by 569 · see citing works

Evaluating the Performance of ChatGPT in Ophthalmology

Antaki, F., Touma, S., Milad, D., El-Khoury, J., & Duval, R. (2023) · Ophthalmology Science · doi:10.1016/j.xops.2023.100324 · Cited by 563 · see citing works

ChatGPT makes medicine easy to swallow: an exploratory case study on simplified radiology reports

Jeblick, K., Schachtner, B., Dexl, J., Mittermeier, A., Stüber, A. T., Topalis, J., Weber, T., Wesp, P., Sabel, B. O., Ricke, J., et al. (2023) · European Radiology · doi:10.1007/s00330-023-10213-1 · Cited by 549 · see citing works

Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models

Zhang, Y., Li, Y., Cui, L., Cai, D., Liu, L., Fu, T., Huang, X., Zhao, E., Zhang, Y., Xu, C., et al. (2023) · arXiv · doi:10.48550/arxiv.2309.01219 · Cited by 544 · see citing works

Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

(2018) · doi:10.18653/v1/n18-1 · Cited by 539 · see citing works

Synthetic and Natural Noise Both Break Neural Machine TranslationPreprint

Belinkov, Y., & Bisk, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.02173 · Cited by 533 · see citing works

Code Llama: Open Foundation Models for Code

Rozière, B., Gehring, J., Gloeckle, F., Sootla, S., Gat, I., Tan, X. E., Adi, Y., Liu, J., Sauvestre, R., Remez, T., et al. (2023) · arXiv · doi:10.48550/arxiv.2308.12950 · Cited by 531 · see citing works

Artificial General Intelligence: Concept, State of the Art, and Future Prospects

Goertzel, B. (2014) · Journal of Artificial General Intelligence · doi:10.2478/jagi-2014-0001 · Cited by 525 · see citing works

ChatGPT Goes to Law SchoolPreprint

Choi, J. H., Hickman, K. E., Monahan, A., & Schwarcz, D. B. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4335905 · Cited by 522 · see citing works

Is ChatGPT a General-Purpose Natural Language Processing Task Solver?

Qin, C., Zhang, A., Zhang, Z., Chen, J., Yasunaga, M., & Yang, D. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.85 · Cited by 519 · see citing works

Calibrate Before Use: Improving Few-Shot Performance of Language Models

Zhao, T. Z., Wallace, E., Feng, S., Klein, D., & Singh, S. (2021) · arXiv · doi:10.48550/arxiv.2102.09690 · Cited by 513 · see citing works

Capabilities of GPT-4 on Medical Challenge Problems

Nori, H., King, N., McKinney, S. M., Carignan, D., & Horvitz, E. (2023) · arXiv · doi:10.48550/arxiv.2303.13375 · Cited by 509 · see citing works

MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance

Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C. M., & Eger, S. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1053 · Cited by 490 · see citing works

Unified Structure Generation for Universal Information Extraction

Lu, Y., Liu, Q., Dai, D., Xiao, X., Lin, H., Han, X., Sun, L., & Wu, H. R. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.395 · Cited by 481 · see citing works

Holistic Evaluation of Language Models

Bommasani, R., Liang, P., & Lee, T. (2023) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.15007 · Cited by 480 · see citing works

HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face

Shen, Y., Song, K., Tan, X., Li, D., Lu, W., & Zhuang, Y. (2023) · arXiv · doi:10.48550/arxiv.2303.17580 · Cited by 477 · see citing works

FinGPT: Open-Source Financial Large Language Models

Yang, H., Liu, X.-Y., & Wang, C. D. (2023) · doi:10.2139/ssrn.4489826 · Cited by 475 · see citing works

Regulating ChatGPT and other Large Generative AI Models

Hacker, P., Engel, A., & Mauer, M. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3593013.3594067 · Cited by 466 · see citing works

CodeT5+: Open Code Large Language Models for Code Understanding and Generation

Wang, Y., Le, H., Gotmare, A., Bui, N., Li, J., & Hoi, S. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.68 · Cited by 459 · see citing works

CodeT5+: Open Code Large Language Models for Code Understanding and Generation

Wang, Y., Le, H., Gotmare, A. D., Bui, N. D. Q., Li, J., & Hoi, S. C. H. (2023) · arXiv · doi:10.48550/arxiv.2305.07922 · Cited by 459

Neural Machine Translation: A Review

Stahlberg, F. (2020) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12007 · Cited by 453 · see citing works

A Survey on Data Augmentation for Text Classification

Bayer, M., Kaufhold, M., & Reuter, C. (2022) · ACM Computing Surveys · doi:10.1145/3544558 · Cited by 453 · see citing works

Enriching Pre-trained Language Model with Entity Information for Relation Classification

Wu, S., & He, Y. (2019) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3357384.3358119 · Cited by 447 · see citing works

GPTScore: Evaluate as You Desire

Fu, J., Ng, S.-K., Jiang, Z., & Liu, P. (2024) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2024.naacl-long.365 · Cited by 446 · see citing works

Event Extraction by Answering (Almost) Natural Questions

Du, X., & Cardie, C. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.49 · Cited by 437 · see citing works

Towards Reasoning in Large Language Models: A Survey

Huang, J., & Chang, K. C.-C. (2023) · Findings of the Association for Computational Linguistics: ACL 2023 · doi:10.18653/v1/2023.findings-acl.67 · Cited by 428 · see citing works

Can ChatGPT forecast stock price movements? Return predictability and large language models

Lopez-Lira, A., & Tang, Y. (2026) · Journal of Financial Economics · doi:10.1016/j.jfineco.2026.104335 · Cited by 425 · see citing works

The Validity of Sentiment Analysis: Comparing Manual Annotation, Crowd-Coding, Dictionary Approaches, and Machine Learning Algorithms

van Atteveldt, W., van der Velden, M. A. C. G., & Boukes, M. (2021) · Communication Methods and Measures · doi:10.1080/19312458.2020.1869198 · Cited by 420 · see citing works

LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding

Bai, Y., Lv, X., Zhang, J., Lyu, H., Tang, J., Huang, Z., Du, Z., Liu, X., Zeng, A., Hou, L., et al. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2024.acl-long.172 · Cited by 400 · see citing works

Self-Alignment Pretraining for Biomedical Entity Representations

Liu, F., Shareghi, E., Meng, Z., Basaldella, M., & Collier, N. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.334 · Cited by 397 · see citing works

Large Language Model as Attributed Training Data Generator: A Tale of Diversity and BiasPreprint

Yu, Y., Zhuang, Y.-C., Zhang, J., Meng, Y., Ratner, A. J., Krishna, R., Shen, J., & Zhang, C. (2023) · Neural Information Processing Systems · doi:10.48550/arxiv.2306.15895 · Cited by 396 · see citing works

Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation

Liu, J., Xia, C. S., Wang, Y., & Zhang, L. (2023) · arXiv · doi:10.48550/arxiv.2305.01210 · Cited by 390 · see citing works

Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation

Liu, J., Xia, C. S., Wang, Y., & Zhang, L. (2023) · Advances in Neural Information Processing Systems 36 · doi:10.52202/075280-0943 · Cited by 384 · see citing works

LexGLUE: A Benchmark Dataset for Legal Language Understanding in English

Chalkidis, I., Jana, A., Hartung, D., Bommarito, M. J., Androutsopoulos, I., Katz, D. M., & Αλέτρας, Ν. (2021) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.297 · Cited by 376 · see citing works

Translating radiology reports into plain language using ChatGPT and GPT-4 with prompt learning: results, limitations, and potential

Lyu, Q., Tan, J., Zapadka, M. E., Ponnatapura, J., Niu, C., Myers, K. J., Wang, G., & Whitlow, C. T. (2023) · Visual Computing for Industry, Biomedicine, and Art · doi:10.1186/s42492-023-00136-5 · Cited by 367 · see citing works

Red Teaming Language Models with Language Models

Perez, E., Huang, S., Song, F., Cai, T., Ring, R., Aslanides, J., Glaese, A., McAleese, N., & Irving, G. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.225 · Cited by 363 · see citing works

Crosslingual Generalization through Multitask Finetuning

Muennighoff, N., Wang, T., Sutawika, L., Roberts, A., Biderman, S., Le Scao, T., Bari, M. S., Shen, S., Yong, Z. X., Schoelkopf, H., et al. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.891 · Cited by 361 · see citing works

Large language models are few-shot clinical information extractors

Agrawal, M., Hegselmann, S., Lang, H., Kim, Y., & Sontag, D. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.130 · Cited by 358 · see citing works

Data augmentation approaches in natural language processing: A survey

Li, B., Hou, Y., & Che, W. (2022) · AI Open · doi:10.1016/j.aiopen.2022.03.001 · Cited by 357 · see citing works

BloombergGPT: A Large Language Model for Finance

Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., & Mann, G. (2023) · arXiv · Cited by 357 · see citing works

BARTScore: Evaluating Generated Text as Text Generation

Yuan, W., Neubig, G., & Liu, P. (2021) · arXiv · doi:10.48550/arxiv.2106.11520 · Cited by 353 · see citing works

FinBERT: A Pretrained Language Model for Financial CommunicationsPreprint

Yang, Y., Mark Christopher Siy, U. Y., & Huang, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.08097 · Cited by 346 · see citing works

Diagnostic Accuracy of Differential-Diagnosis Lists Generated by Generative Pretrained Transformer 3 Chatbot for Clinical Vignettes with Common Chief Complaints: A Pilot Study

Hirosawa, T., Harada, Y., Yokose, M., Sakamoto, T., Kawamura, R., & Shimizu, T. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20043378 · Cited by 345 · see citing works

HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models

Li, J., Cheng, X., Zhao, X., Nie, J.-Y., & Wen, J.-R. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.397 · Cited by 343 · see citing works

Multilingual Translation with Extensible Multilingual Pretraining and FinetuningPreprint

Tang, Y., Tran, C., Li, X., Chen, P., Goyal, N., Chaudhary, V., Gu, J., & Fan, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.00401 · Cited by 341 · see citing works

Towards Expert-Level Medical Question Answering with Large Language Models

Singhal, K., Tu, T., Gottweis, J., Sayres, R., Wulczyn, E., Hou, L., Clark, K., Pfohl, S., Cole-Lewis, H., Neal, D., et al. (2023) · arXiv · doi:10.48550/arxiv.2305.09617 · Cited by 338 · see citing works

An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA

Yang, Z., Gan, Z., Wang, J., Hu, X., Lu, Y., Liu, Z., & Wang, L. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i3.20215 · Cited by 334 · see citing works

Learning to Generate Reviews and Discovering Sentiment

Radford, A., Jozefowicz, R., & Sutskever, I. (2017) · arXiv · doi:10.48550/arxiv.1704.01444 · Cited by 330 · see citing works

CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis

Nijkamp, E., Pang, B., Hayashi, H., Tu, L., Wang, H., Zhou, Y., Savarese, S., & Xiong, C. (2022) · arXiv · doi:10.48550/arxiv.2203.13474 · Cited by 330 · see citing works

FinBERT: A Pre-trained Financial Language Representation Model for Financial Text Mining

Liu, Z., Huang, D., Huang, K., Li, Z., & Zhao, J. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/622 · Cited by 327 · see citing works

Large Language Models are Zero-Shot Rankers for Recommender Systems

Hou, Y., Zhang, J., Lin, Z., Lu, H., Xie, R., McAuley, J., & Zhao, W. X. (2024) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-56060-6_24 · Cited by 327 · see citing works

That's So Annoying!!!: A Lexical and Frame-Semantic Embedding Based Data Augmentation Approach to Automatic Categorization of Annoying Behaviors using #petpeeve Tweets

Wang, W. Y., & Yang, D. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1306 · Cited by 323 · see citing works

Natural Language Processing Advancements By Deep Learning: A SurveyPreprint

Torfi, A., Shirvani, R. A., Keneshloo, Y., Tavaf, N., & Fox, E. A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.01200 · Cited by 322 · see citing works

How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection

Guo, B., Zhang, X., Wang, Z., Jiang, M., Nie, J., Ding, Y., Yue, J., & Wu, Y. (2023) · arXiv · doi:10.48550/arxiv.2301.07597 · Cited by 318 · see citing works

Is ChatGPT A Good Translator? Yes With GPT-4 As The Engine

Jiao, W., Wang, W., Huang, J.-T., Wang, X., Shi, S., & Tu, Z. (2023) · arXiv · doi:10.48550/arxiv.2301.08745 · Cited by 317 · see citing works

SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language ModelsPreprint

Wang, X.-X., Hu, Z.-N., Lu, P., Zhu, Y., Zhang, J., Subramaniam, S., Loomba, A. R., Zhang, S., Sun, Y., & Wang, W. (2023) · International Conference on Machine Learning · doi:10.48550/arxiv.2307.10635 · Cited by 300 · see citing works

Red-Teaming Large Language Models using Chain of Utterances for Safety-AlignmentPreprint

Bhardwaj, R., & Poria, S. (2023) · arXiv.org · doi:10.48550/arxiv.2308.09662 · Cited by 292 · see citing works

All That’s ‘Human’ Is Not Gold: Evaluating Human Evaluation of Generated Text

Clark, E., August, T., Serrano, S., Haduong, N., Gururangan, S., & Smith, N. A. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.565 · Cited by 290 · see citing works

All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Text

Clark, E., August, T., Serrano, S., Haduong, N., Gururangan, S., & Smith, N. A. (2021) · arXiv · doi:10.48550/arxiv.2107.00061 · Cited by 290

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Smith, S., Patwary, M., Norick, B., LeGresley, P., Rajbhandari, S., Casper, J., Liu, Z., Prabhumoye, S., Zerveas, G., Korthikanti, V., et al. (2022) · arXiv · doi:10.48550/arxiv.2201.11990 · Cited by 286 · see citing works

Chain-of-Verification Reduces Hallucination in Large Language Models

Dhuliawala, S., Komeili, M., Xu, J., Raileanu, R., Li, X., Celikyilmaz, A., & Weston, J. (2024) · Findings of the Association for Computational Linguistics ACL 2024 · doi:10.18653/v1/2024.findings-acl.212 · Cited by 282 · see citing works

Assessing the accuracy of responses by the language model ChatGPT to questions regarding bariatric surgery

Samaan, J. S., Yeo, Y. H., Rajeev, N., Hawley, L., Abel, S., Ng, W. H., Srinivasan, N., Park, J., Burch, M., Watson, R., et al. (2023) · Obesity Surgery · doi:10.1007/s11695-023-06603-5 · Cited by 282 · see citing works

AMMU: A survey of transformer-based biomedical pretrained language models

Kalyan, K. S., Rajasekharan, A., & Sangeetha, S. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103982 · Cited by 280 · see citing works

ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection

Hartvigsen, T., Gabriel, S., Palangi, H., Sap, M., Ray, D., & Kamar, E. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.234 · Cited by 279 · see citing works

Automated Program Repair via Conversation: Fixing 162 out of 337 Bugs for $0.42 Each using ChatGPT

Xia, C. S., & Zhang, L. (2024) · ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) · doi:10.1145/3650212.3680323 · Cited by 279 · see citing works

Text Classification via Large Language Models

Sun, X., Li, X., Li, J., Wu, F. Q., Guo, S., Zhang, T., & Wang, G. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.603 · Cited by 273 · see citing works

A Survey on Model Compression for Large Language Models

Zhu, X., Li, J., Liu, Y., Ma, C., & Wang, W. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00704 · Cited by 273 · see citing works

SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities

Zhang, D., Li, S., Zhang, X., Zhan, J., Wang, P., Zhou, Y., & Qiu, X. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.1055 · Cited by 268 · see citing works

Automatic Detection of Generated Text is Easiest when Humans are Fooled

Ippolito, D., Duckworth, D., Callison-Burch, C., & Eck, D. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.164 · Cited by 264 · see citing works

Automatic Detection of Generated Text is Easiest when Humans are Fooled

Ippolito, D., Duckworth, D., Callison-Burch, C., & Eck, D. (2019) · arXiv · doi:10.48550/arxiv.1911.00650 · Cited by 264

StarCoder: may the source be with you!

Li, R., Allal, L. B., Zi, Y., Muennighoff, N., Kocetkov, D., Mou, C., Marone, M., Akiki, C., Li, J., Chim, J., et al. (2023) · arXiv · doi:10.48550/arxiv.2305.06161 · Cited by 261 · see citing works

Will ChatGPT get you caught? Rethinking of Plagiarism Detection

Khalil, M., & Er, E. (2023) · doi:10.35542/osf.io/fnh48 · Cited by 259 · see citing works

FinQA: A Dataset of Numerical Reasoning over Financial Data

Chen, Z., Chen, W., Smiley, C., Shah, S., Borova, I., Langdon, D., Moussa, R., Beane, M., Huang, T.-H., Routledge, B., et al. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.300 · Cited by 258 · see citing works

Towards Out-Of-Distribution Generalization: A Survey

Liu, J., Shen, Z., He, Y., Zhang, X., Xu, R., Yu, H., & Cui, P. (2021) · arXiv · doi:10.48550/arxiv.2108.13624 · Cited by 253 · see citing works

CodeGen2: Lessons for Training LLMs on Programming and Natural LanguagesPreprint

Nijkamp, E., Hayashi, H., Xiong, C.-M., Savarese, S., & Zhou, Y.-B. (2023) · arXiv.org · doi:10.48550/arxiv.2305.02309 · Cited by 253 · see citing works

A Survey on In-context Learning

Dong, Q., Li, L., Dai, D., Zheng, C., Ma, J., Li, R., Xia, H., Xu, J., Wu, Z., Liu, T., et al. (2023) · arXiv · doi:10.48550/arxiv.2301.00234 · Cited by 248 · see citing works

Is ChatGPT better than Human Annotators? Potential and Limitations of ChatGPT in Explaining Implicit Hate Speech

Huang, F., Kwak, H., & An, J. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543873.3587368 · Cited by 247 · see citing works

Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks

Wang, Y., Mishra, S., Alipoormolabashi, P., Kordi, Y., Mirzaei, A., Naik, A., Ashok, A., Dhanasekaran, A. S., Arunkumar, A., Stap, D., et al. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.340 · Cited by 247 · see citing works

BRIO: Bringing Order to Abstractive Summarization

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No More Manual Tests? Evaluating and Improving ChatGPT for Unit Test GenerationPreprint

Yuan, Z.-Q., Lou, Y., Liu, M.-W., Ding, S., Wang, K.-X., Chen, Y.-X., & Peng, X. (2023) · arXiv.org · doi:10.48550/arxiv.2305.04207 · Cited by 239 · see citing works

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ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning

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Ahuja, K., Diddee, H., Hada, R., Ochieng, M., Ramesh, K., Jain, P., Nambi, A. U., Ganu, T., Segal, S., Ahmed, M., et al. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.258 · Cited by 209 · see citing works

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Husein, R. A., Aburajouh, H., & Catal, C. (2025) · Computer Standards & Interfaces · doi:10.1016/j.csi.2024.103917 · Cited by 72 · see citing works

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Pudasaini, S., Miralles-Pechuán, L., Lillis, D., & Llorens Salvador, M. (2025) · Journal of Academic Ethics · doi:10.1007/s10805-024-09576-x · Cited by 70 · see citing works

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Large Language Models in Systematic Review Screening: Opportunities, Challenges, and Methodological Considerations

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Perceptions of Artificial Intelligence and ChatGPT by Speech-Language Pathologists and Students

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Generative AI in the Construction Industry: A State-of-the-art Analysis

Taiwo, R., Bello, I. T., Abdulai, S. F., Yussif, A.-M., Salami, B. A., Saka, A., & Zayed, T. (2024) · arXiv · doi:10.48550/arxiv.2402.09939 · Cited by 21 · see citing works

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Cavalcanti, L. P. S. B., Bezerra, P. T. L., & Barros, G. A. B. (2025) · Anais Estendidos do XXIV Simpósio Brasileiro de Jogos e Entretenimento Digital (SBGames 2025) · doi:10.5753/sbgames_estendido.2025.10149

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Review on Research of Marxism Popularity in 2008

Xia, T. (2009) · Journal of Bohai University

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Aronoff, C. E., McClure, S. L., & Ward, J. L. (2011) · Palgrave Macmillan US eBooks · doi:10.1007/978-1-137-51208-6_8

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Otten‐Pappas, D. (2013) · Journal of Family Business Management · doi:10.1108/20436231311326463 · Cited by 36 · see citing works