PADA: Example-based Prompt Learning for on-the-fly Adaptation to Unseen Domains

Attention Is All You Need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works

Adam: A Method for Stochastic Optimization

Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works

Language Models are Few-Shot Learners

Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372 · 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

Untitled work

(2004) · Cited by 9,718 · 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

Transformers: State-of-the-Art Natural Language Processing

Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/2020.emnlp-demos.6 · Cited by 8,741 · see citing works

Domain-Adversarial Training of Neural Networks

Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2017) · Advances in Computer Vision and Pattern Recognition · doi:10.1007/978-3-319-58347-1_10 · Cited by 8,318 · see citing works

Mining and summarizing customer reviews

Hu, M., & Liu, B. (2004) · Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1014052.1014073 · Cited by 7,793 · 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

Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing

Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023) · ACM Computing Surveys · doi:10.1145/3560815 · Cited by 4,361 · see citing works

Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing

Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2021) · arXiv · doi:10.48550/arxiv.2107.13586 · Cited by 4,352 · see citing works

A large annotated corpus for learning natural language inference

Bowman, S. R., Angeli, G., Potts, C., & Manning, C. D. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1075 · Cited by 3,377 · see citing works

A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference

Williams, A., Nangia, N., & Bowman, S. (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/n18-1101 · Cited by 3,342 · see citing works

The Power of Scale for Parameter-Efficient Prompt Tuning

Lester, B., Al-Rfou, R., & Constant, N. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.243 · Cited by 3,096 · see citing works

The Power of Scale for Parameter-Efficient Prompt Tuning

Lester, B., Al-Rfou, R., & Constant, N. (2021) · arXiv · doi:10.48550/arxiv.2104.08691 · Cited by 3,092

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

Prefix-Tuning: Optimizing Continuous Prompts for Generation

Li, X. L., & Liang, P. (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.353 · Cited by 2,473 · see citing works

Untitled work

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

SemEval-2014 Task 4: Aspect Based Sentiment Analysis

Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., & Manandhar, S. (2014) · International Workshop on Semantic Evaluation (SemEval) · doi:10.3115/v1/s14-2004 · Cited by 1,871 · see citing works

Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach

Glorot, X., Bordes, A., & Bengio, Y. (2011) · Cited by 1,569 · see citing works

Domain adaptation with structural correspondence learning

Blitzer, J., McDonald, R., & Pereira, F. (2006) · Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing - EMNLP '06 · doi:10.3115/1610075.1610094 · Cited by 1,543 · see citing works

Making Pre-trained Language Models Better Few-shot Learners

Gao, T., Fisch, A., & Chen, D. (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.295 · Cited by 1,380 · see citing works

Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference

Schick, T., & Schütze, H. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume · doi:10.18653/v1/2021.eacl-main.20 · Cited by 1,305 · see citing works

AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts

Shin, T., Razeghi, Y., Logan IV, R. L., Wallace, E., & Singh, S. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.346 · Cited by 1,279 · see citing works

GPT understands, too

Liu, X., Zheng, Y., Du, Z., Ding, M., Qian, Y., Yang, Z., & Tang, J. (2023) · AI Open · doi:10.1016/j.aiopen.2023.08.012 · Cited by 1,124 · see citing works

How Can We Know What Language Models Know?

Jiang, Z., Xu, F. F., Araki, J., & Neubig, G. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00324 · Cited by 1,002 · see citing works

Invariant Risk Minimization

Arjovsky, M., Bottou, L., Gulrajani, I., & Lopez-Paz, D. (2019) · arXiv · doi:10.48550/arxiv.1907.02893 · Cited by 927 · see citing works

Domain Adaptation for Statistical Classifiers

Daume III, H., & Marcu, D. (2006) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1872 · Cited by 816 · see citing works

Cross-domain sentiment classification via spectral feature alignment

Pan, S. J., Ni, X., Sun, J.-T., Yang, Q., & Chen, Z. (2010) · International World Wide Web Conference (WWW) · doi:10.1145/1772690.1772767 · Cited by 791 · see citing works

Domain Generalization via Invariant Feature Representation

Muandet, K., Balduzzi, D., & Schölkopf, B. (2013) · arXiv · doi:10.48550/arxiv.1301.2115 · Cited by 771 · see citing works

Marginalized Denoising Autoencoders for Domain AdaptationPreprint

Chen, M., Xu, Z., Weinberger, K. Q., & Sha, F. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1206.4683 · Cited by 742 · see citing works

Analysing How People Orient to and Spread Rumours in Social Media by Looking at Conversational Threads

Zubiaga, A., Liakata, M., Procter, R., Wong Sak Hoi, G., & Tolmie, P. (2016) · PLOS ONE · doi:10.1371/journal.pone.0150989 · Cited by 722 · 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

WILDS: A Benchmark of in-the-Wild Distribution Shifts

Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., et al. (2020) · arXiv · doi:10.48550/arxiv.2012.07421 · Cited by 518 · see citing works

PTR: Prompt Tuning with Rules for Text Classification

Han, X., Zhao, W., Ding, N., Liu, Z., & Sun, M. (2022) · AI Open · doi:10.1016/j.aiopen.2022.11.003 · Cited by 503 · see citing works

PTR: Prompt Tuning with Rules for Text Classification

Han, X., Zhao, W., Ding, N., Liu, Z., & Sun, M. (2021) · arXiv · doi:10.48550/arxiv.2105.11259 · Cited by 478 · see citing works

MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer

Pfeiffer, J., Vulić, I., Gurevych, I., & Ruder, S. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.617 · Cited by 467 · see citing works

Exploiting Context for Rumour Detection in Social Media

Zubiaga, A., Liakata, M., & Procter, R. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-67217-5_8 · Cited by 350 · see citing works

Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence ModelsPreprint

Vijayakumar, A. K., Cogswell, M., Selvaraju, R. R., Sun, Q., Lee, S., Crandall, D., & Batra, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.02424 · Cited by 350 · see citing works

Neural Unsupervised Domain Adaptation in NLP—A Survey

Ramponi, A., & Plank, B. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.603 · Cited by 253 · see citing works

How many data points is a prompt worth?

Le Scao, T., & Rush, A. (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.208 · Cited by 179 · see citing works

Multi-Source Domain Adaptation with Mixture of Experts

Guo, J., Shah, D., & Barzilay, R. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1498 · Cited by 168 · see citing works

Distributionally Robust Neural Networks

Sagawa, S., Koh, P. W., Hashimoto, T., & Liang, P. (2020) · International Conference on Learning Representations · Cited by 164 · see citing works

Sentence and Expression Level Annotation of Opinions in User-Generated Discourse

Toprak, C., Jakob, N., & Gurevych, I. (2010) · TUbilio (Technical University of Darmstadt) · Cited by 158 · see citing works

Automatic Domain Adaptation for Parsing

McClosky, D., Eugene Charniak, D., & Johnson, M. (2010) · Cited by 155 · see citing works

Unsupervised Domain Adaptation of Contextualized Embeddings for Sequence Labeling

Han, X., & Eisenstein, J. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1433 · Cited by 149 · see citing works

Pivot Based Language Modeling for Improved Neural Domain Adaptation

Ziser, Y., & Reichart, R. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1112 · Cited by 139 · see citing works

Multi-domain sentiment classification

Li, S., & Zong, C. (2008) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/1557690.1557765 · Cited by 130 · see citing works

Multinomial Adversarial Networks for Multi-Domain Text Classification

Chen, X., & Cardie, C. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1111 · Cited by 122 · see citing works

Neural Structural Correspondence Learning for Domain Adaptation

Ziser, Y., & Reichart, R. (2017) · Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017) · doi:10.18653/v1/k17-1040 · Cited by 122 · see citing works

Transfer learning from multiple source domains via consensus regularization

Luo, P., Zhuang, F., Xiong, H., Xiong, Y., & He, Q. (2008) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1458082.1458099 · Cited by 117 · see citing works

Does Distributionally Robust Supervised Learning Give Robust Classifiers?Preprint

Hu, W., Niu, G., Sato, I., & Sugiyama, M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.02041 · Cited by 112 · see citing works

Supervised and unsupervised PCFG adaptation to novel domains

Roark, B., & Bacchiani, M. (2003) · doi:10.3115/1073445.1073472 · Cited by 94 · see citing works

Self-Training for Enhancement and Domain Adaptation of Statistical Parsers Trained on Small Datasets

Reichart, R., & Rappoport, A. (2007) · Cited by 94 · see citing works

Zero-Shot Deep Domain Adaptation

Peng, K., Wu, Z., & Ernst, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01252-6_47 · Cited by 90 · see citing works

Untitled work

(2019) · doi:10.18653/v1/d19-1432 · Cited by 88 · see citing works

BERTese: Learning to Speak to BERT

Haviv, A., Berant, J., & Globerson, A. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume · doi:10.18653/v1/2021.eacl-main.316 · Cited by 84 · see citing works

Unified Feature and Instance Based Domain Adaptation for Aspect-Based Sentiment Analysis

Gong, C., Yu, J., & Xia, R. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.572 · Cited by 69 · see citing works

Domain Robustness in Neural Machine Translation

Müller, M., Rios, A., & Sennrich, R. (2019) · arXiv · doi:10.48550/arxiv.1911.03109 · Cited by 68 · see citing works

On Calibration and Out-of-domain GeneralizationPreprint

Wald, Y., Feder, A., Greenfeld, D., & Shalit, U. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.10395 · Cited by 59 · see citing works

Multiple Source Domain Adaptation with Adversarial Learning

Zhao, H., Zhang, S., Wu, G., Costeira, J. P., Moura, J. M. F., & Gordon, G. J. (2018) · International Conference on Learning Representations · Cited by 58 · see citing works

PERL: Pivot-based Domain Adaptation for Pre-trained Deep Contextualized Embedding Models

Ben-David, E., Rabinovitz, C., & Reichart, R. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00328 · Cited by 52 · see citing works

FLORS: Fast and Simple Domain Adaptation for Part-of-Speech Tagging

Schnabel, T., & Schütze, H. (2014) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00162 · Cited by 48 · see citing works

Transformer Based Multi-Source Domain Adaptation

Wright, D., & Augenstein, I. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.639 · Cited by 48 · see citing works

Task Refinement Learning for Improved Accuracy and Stability of Unsupervised Domain Adaptation

Ziser, Y., & Reichart, R. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1591 · Cited by 36 · see citing works

DILBERT: Customized Pre-Training for Domain Adaptation with Category Shift, with an Application to Aspect Extraction

Lekhtman, E., Ziser, Y., & Reichart, R. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.20 · Cited by 30 · see citing works

Fast Easy Unsupervised Domain Adaptation with Marginalized Structured Dropout

Yang, Y., & Eisenstein, J. (2014) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/v1/p14-2088 · Cited by 29 · see citing works

Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2012, July 12-14, 2012, Jeju Island, Korea

Heafield, K., Koehn, P., & Lavie, A. (2012) · The Association for Computational Linguistics · Cited by 29 · see citing works

MultiCQA: Zero-Shot Transfer of Self-Supervised Text Matching Models on a Massive Scale

Rücklé, A., Pfeiffer, J., & Gurevych, I. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.194 · Cited by 26 · see citing works

ACL 2007, Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics, June 23-30, 2007, Prague, Czech Republic

Cohn, T., & Lapata, M. (2007) · Cited by 15 · see citing works

Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics, Proceedings, June 2-4, 2010, Los Angeles, California, USA

Das, D., Schneider, N., Chen, D., & Smith, N. A. (2010) · The Association for Computational Linguistics · Cited by 11 · see citing works

Conditioned Natural Language Generation using only Unconditioned Language Model: An ExplorationPreprint

Sun, F.-K., & Lai, C.-I. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.07347 · Cited by 9 · see citing works

Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing

Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023) · ACM Computing Surveys · doi:10.1145/3560815 · Cited by 4,361 · see citing works

Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing

Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2021) · arXiv · doi:10.48550/arxiv.2107.13586 · Cited by 4,352 · see citing works

A Comprehensive Survey on Test-Time Adaptation Under Distribution Shifts

Liang, J., He, R., & Tan, T.-P. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-024-02181-w · Cited by 693 · see citing works

Personalized Prompt Learning for Explainable Recommendation

Li, L., Zhang, Y., & Chen, L. (2023) · ACM Transactions on Information Systems · doi:10.1145/3580488 · Cited by 172 · see citing works

Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey

Ling, C., Zhao, X., Lu, J., Deng, C., Zheng, C., Wang, J., Chowdhury, T., Li, Y., Cui, H., Zhang, X., et al. (2025) · ACM Computing Surveys · doi:10.1145/3764579 · Cited by 263 · see citing works

Prompt Distillation for Efficient LLM-based Recommendation

Li, L., Zhang, Y., & Chen, L. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3615017 · Cited by 144 · see citing works

Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data

Shum, K., Diao, S., & Zhang, T. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.811 · Cited by 65 · see citing works

ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Prediction

Lin, J., Chen, B., Wang, H., Xi, Y., Qu, Y., Dai, X., Zhang, K., Tang, R., Yu, Y. H., & Zhang, W. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589334.3645396 · Cited by 52 · see citing works

Domain Prompt Learning for Efficiently Adapting CLIP to Unseen Domains

Zhang, X., Gu, S., Matsuo, Y., & Iwasawa, Y. (2023) · Transactions of the Japanese Society for Artificial Intelligence · doi:10.1527/tjsai.38-6_b-mc2 · Cited by 43 · see citing works

Incremental accumulation of linguistic context in artificial and biological neural networks

Tikochinski, R., Goldstein, A., Meiri, Y., Hasson, U., & Reichart, R. (2025) · Nature Communications · doi:10.1038/s41467-025-56162-9 · Cited by 24 · see citing works

Prompting Strategies for Citation Classification

Kunnath, S. N., Pride, D., & Knoth, P. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3615018 · Cited by 19 · see citing works

The Life Cycle of Knowledge in Big Language Models: A Survey

Cao, B., Lin, H., Han, X., & Sun, L. (2023) · arXiv · doi:10.48550/arxiv.2303.07616 · Cited by 18

A prompt construction method for the reverse dictionary task of large-scale language models

Tian, S., Huang, S., Li, R., & Wei, C. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108596 · Cited by 19 · see citing works

Knowledge Graph-Based In-Context Learning for Advanced Fault Diagnosis in Sensor Networks

Xie, X., Wang, J., Han, Y., & Li, W. (2024) · Sensors · doi:10.3390/s24248086 · Cited by 16 · see citing works

Graph-Enhanced Prompt Learning for Personalized Review Generation

Qu, X., Wang, Y., Zhao, L., & Gao, J. (2024) · Data Science and Engineering · doi:10.1007/s41019-024-00252-z · Cited by 15 · see citing works

Zero-Shot Domain-Sensitive Speech Recognition with Prompt-Conditioning Fine-Tuning

Liao, F.-T., Chan, Y.-C., Chen, Y., Hsu, C.-J., & Shiu, D.-S. (2023) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru57964.2023.10389617 · Cited by 13 · see citing works

A Cross-Domain Generative Data Augmentation Framework for Aspect-Based Sentiment Analysis

Xue, J., Li, Y., Li, Z., Cui, Y., Zhang, S., & Wang, S. (2023) · Electronics · doi:10.3390/electronics12132949 · Cited by 11 · see citing works

Prompt-Augmented Linear Probing: Scaling beyond the Limit of Few-Shot In-Context Learners

Cho, H., Kim, H. J., Kim, J., Lee, S.-W., Lee, S., Yoo, K. M., & Kim, T. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i11.26495 · Cited by 11 · see citing works

On the Robustness of Dialogue History Representation in Conversational Question Answering: A Comprehensive Study and a New Prompt-based Method

Gekhman, Z., Oved, N., Keller, O., Szpektor, I., & Reichart, R. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00549 · Cited by 9 · see citing works

Generalizing Few-Shot Named Entity Recognizers to Unseen Domains with Type-Related Features

Wang, Z., Zhao, Z., Chen, Z., Ren, P., de Rijke, M., & Ren, Z. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.147 · Cited by 9 · see citing works

Exploring Pre-Trained Language Models to Build Knowledge Graph for Metal-Organic Frameworks (MOFs)

An, Y., Greenberg, J., Hu, X. T., Kalinowski, A., Fang, X., Zhao, X., McClellan, S., Uribe‐Romo, F. J., Langlois, K., Furst, J. D., et al. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020568 · Cited by 9 · see citing works

Prompt-Based Word-Level Information Injection BERT for Chinese Named Entity Recognition

He, Q., Chen, G., Song, W., & Zhang, P. (2023) · Applied Sciences · doi:10.3390/app13053331 · Cited by 9 · see citing works

Genetic Prompt Search via Exploiting Language Model Probabilities

Zhao, J., Wang, Z., & Yang, F. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/588 · Cited by 9 · see citing works

A Communication Theory Perspective on Prompting Engineering Methods for Large Language Models

Song, Y., He, Y., Zhao, X.-F., Gu, H., Jiang, D., Yang, H., & Fan, L. (2024) · Journal of Computer Science and Technology · doi:10.1007/s11390-024-4058-8 · Cited by 9 · see citing works

ChatICD: Prompt Learning for Few-shot ICD Coding through ChatGPT

Liu, J., Yang, S., Peng, T., Hu, X., & Zhu, Q. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385482 · Cited by 8 · see citing works

Prompt-based Distribution Alignment for Domain Generalization in Text Classification

Chen, J., & Zhang, Y. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.690 · Cited by 6 · see citing works

ClusterPrompt: Cluster Semantic Enhanced Prompt Learning for New Intent Discovery

Liang, J., & Liao, L. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.702 · Cited by 6 · see citing works

Soft prompt enhanced joint learning for cross-domain aspect-based sentiment analysis

Shi, J., Li, W., Bai, Q., Yang, Y., & Jiang, J. (2023) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2023.200292 · Cited by 7 · see citing works

Chain-of-Situation Aware Progressive Inference Learning

Liu, Y., Liu, F., Jiao, L., Bao, Q., Li, S., Li, L., Liu, X., Chen, P., & Ma, W. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3576486 · Cited by 5 · see citing works

PMRC: Prompt-Based Machine Reading Comprehension for Few-Shot Named Entity Recognition

Huang, J., Yan, D., & Cai, Y. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i16.29791 · Cited by 5 · see citing works

Distinguishability Calibration to In-Context Learning

Li, H., Yan, H., Li, Y., Qian, L., He, Y., & Gui, L. (2023) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2023.findings-eacl.102 · Cited by 7 · see citing works

Towards Distribution-shift Robust Text Classification of Emotional Content

Bulla, L., Aldo Gangemi, A., & Mongiovı̀, M. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.524 · Cited by 4 · see citing works

CORT: A New Baseline for Comparative Opinion Classification by Dual Prompts

Wang, Y., Zhang, H., Sun, A., & Meng, X. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.524 · Cited by 4 · see citing works

Zero-shot Transfer of Article-aware Legal Outcome Classification for European Court of Human Rights Cases

T.y.s.s, S., Ichim, O., & Grabmair, M. (2023) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2023.findings-eacl.44 · Cited by 10 · see citing works

Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data

Shum, K., Diao, S., & Zhang, T. (2023) · arXiv · doi:10.48550/arxiv.2302.12822 · Cited by 3 · see citing works

PROMPT-BART: A Named Entity Recognition Model Applied to Cyber Threat Intelligence

Feng, X., He, S., Wei, X., Liu, R., Yue, H., & Wang, X. (2025) · Applied Sciences · doi:10.3390/app151810276 · Cited by 3 · see citing works

Adaptive multimodal prompt for human-object interaction with local feature enhanced transformer

Xue, K., Gao, Y., Fang, Z., Jiang, X., Yu, W., Chen, M., & Wu, C. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05774-7 · Cited by 3 · see citing works

Prompt Learning for Multi-modal COVID-19 Diagnosis

Yu, Y., Rong, L., Wang, M., Huang, M., Zhang, Y., & Ding, Y. (2022) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm55620.2022.9995157 · Cited by 3 · see citing works

Memory-Based Invariance Learning for Out-of-Domain Text Classification

Chen, J., & Zhang, Y. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.101 · Cited by 2 · see citing works

A Robust Information-Masking Approach for Domain Counterfactual Generation

Hong, P., Bhardwaj, R., Majumder, N., Aditya, S., & Poria, S. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.231 · Cited by 2 · see citing works

Speaking the Language of Your Listener: Audience-Aware Adaptation via Plug-and-Play Theory of Mind

Takmaz, E., Brandizzi, N., Giulianelli, M., Pezzelle, S., & Fernández, R. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.258 · Cited by 2 · see citing works

Mixture of Hybrid Prompts for Cross-Domain Aspect Sentiment Triplet Extraction

Yang, F., Zhou, X., Zhang, M., & Zhou, G. (2024) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2024.3487870 · Cited by 2 · see citing works

MixPro: Simple yet Effective Data Augmentation for Prompt-based Learning

Li, B., Dou, L., Hou, Y., Feng, Y., Mu, H., Wang, E., Zhu, Q., Sun, Q., & Che, W. (2025) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-025-02548-6 · Cited by 2 · see citing works

Aspect-Enhanced Prompting Method for Unsupervised Domain Adaptation in Aspect-Based Sentiment Analysis

Lu, B., Shirai, K., & Kertkeidkachorn, N. (2025) · Information · doi:10.3390/info16050411 · Cited by 1 · see citing works

Context-aware prompting for collaborative problem solving skill identification

Zhu, M., Feng, L., WANG, X., & Huang, W. (2026) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2026.100567 · Cited by 2 · see citing works

Efficient Fine-Tuning for Low-Resource Tibetan Pre-trained Language Models

Zhou, M., Daiqing, Z., Qun, N., & Tashi, N. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72350-6_28 · Cited by 1 · see citing works

Reparameterized and regularized prompt learning based on feature attention for explainable recommendation system

Aamana, A., Iltaf, N., Zia, U., & Khan, Z. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.114519 · Cited by 1 · see citing works

Simple and Effective Input Reformulations for Translation

Yu, B., Lillemark, H. J., & Keutzer, K. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.638

Enhancing Genetic Prompt Search Using Language Model Probability ExploitationPreprint

Song, Z., & Huang, S. (2024) · Research Square · doi:10.21203/rs.3.rs-4511610/v1

Entity Set Expansion Based on Category Prompts in MOOCs

Chen, Y., Wan, J., Du, Y., Yu, J., Hou, L., & Li, J. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5495-3_24

Review of Language Models in the Financial Domain

Wagh, A., & Mavle, A. (2024) · Intelligent Systems and Machine Learning Conference (ISML) · doi:10.1109/isml60050.2024.11007354 · Cited by 1 · see citing works

Prompt-Based Out-of-Distribution Intent Detection

Chow, R., & Lam, A. Y. S. (2025) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2024.3372440

A multi-round MRC framework incorporating prompt learning for aspect sentiment triple extraction

Yuyao, Z., Yan, Z., Zhang, X., & Li, Z. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3456

CFEO: Causal Feature Extraction and Optimization for Cross-Domain Text Classification

Zhang, Y., Han, Y., & Yan, R. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-5722-6_5

Privacy-Enhanced Federated Prompt Tuning Against Backdoor Attack

He, G., Ren, Y., He, G., Feng, G., & Zhang, X. (2026) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2026.3660272

UAM-CFEO: causal feature extraction and optimization for cross-domain text classification with uncertainty-aware memory augmentation

Zhang, Y., Yan, R., & Han, Y. (2026) · World Wide Web · doi:10.1007/s11280-026-01423-w

Evaluation of Inductive Coding with LLMs

Dörfel, L., & Ammoneit, R. (2026) · Education Sciences · doi:10.3390/educsci16081314

PADA: A Prompt-based Autoregressive Approach for Adaptation to Unseen Domains.Preprint

Ben‐David, E., Oved, N., & Reichart, R. (2021) · arXiv (Cornell University) · Cited by 30 · see citing works

Example-based Hypernetworks for Multi-source Adaptation to Unseen Domains

Volk, T., Ben-David, E., Amosy, O., Chechik, G., & Reichart, R. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.610 · Cited by 3 · see citing works

Example-based Hypernetworks for Out-of-Distribution GeneralizationPreprint

Volk, T., Ben-David, E., Amosy, O., Chechik, G., & Reichart, R. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2203.14276 · Cited by 4 · see citing works

PØDA: Prompt-driven Zero-shot Domain AdaptationPreprint

Fahes, M., Vu, T.-H., Bursuc, A., Pérez, P., & de Charette, R. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2212.03241 · Cited by 1 · see citing works

Soft prompt-tuning for unsupervised domain adaptation via self-supervision

Zhu, Y. Q., Wang, S., Li, Y., Yuan, Y.-H., & Qiang, J. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.129008 · Cited by 8 · see citing works