Muppet: Massive Multi-task Representations with Pre-Finetuning

Adam: A Method for Stochastic Optimization

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Rethinking the Inception Architecture for Computer Vision

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RoBERTa: A Robustly Optimized BERT Pretraining Approach

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

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

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Character-level Convolutional Networks for Text ClassificationPreprint

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Building a Large Annotated Corpus of English: The Penn Treebank

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Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank

Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., & Potts, C. (2013) · Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d13-1170 · Cited by 6,878 · see citing works

XLNet: Generalized Autoregressive Pretraining for Language Understanding

Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1906.08237 · Cited by 6,687 · see citing works

SQuAD: 100,000+ Questions for Machine Comprehension of Text

Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1264 · Cited by 6,208 · 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

Learning Word Vectors for Sentiment Analysis

Maas, A. L., Daly, R. E., Pham, P. T., Huang, D., Ng, A. Y., & Potts, C. (2011) · Cited by 3,710 · 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

Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

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Teaching Machines to Read and ComprehendPreprint

Hermann, K. M., Kočiský, T., Grefenstette, E., Espeholt, L., Kay, W., Suleyman, M., & Blunsom, P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.03340 · Cited by 2,779 · see citing works

Bidirectional Attention Flow for Machine ComprehensionPreprint

Seo, M., Kembhavi, A., Farhadi, A., & Hajishirzi, H. (2016) · International Conference on Learning Representations · doi:10.48550/arxiv.1611.01603 · Cited by 2,322 · see citing works

Seeing stars

Pang, B., & Lee, L. (2005) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/1219840.1219855 · Cited by 2,153 · see citing works

Natural Questions: A benchmark for question answering research

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

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HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering

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TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

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The PASCAL Recognising Textual Entailment Challenge

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SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation

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Learning question classifiers

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Multi-Task Deep Neural Networks for Natural Language Understanding

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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

Automatically Constructing a Corpus of Sentential Paraphrases.

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SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems

Wang, A., Pruksachatkun, Y., Nangia, N., Singh, A., Michael, J., Hill, F., Levy, O., & Bowman, S. R. (2019) · arXiv · doi:10.48550/arxiv.1905.00537 · Cited by 1,051 · see citing works

RACE: Large-scale ReAding Comprehension Dataset From Examinations

Lai, G., Xie, Q., Liu, H., Yang, Y., & Hovy, E. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1082 · Cited by 1,044 · see citing works

Neural Network Acceptability Judgments

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MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms

Amini, A., Gabriel, S., Lin, S., Koncel-Kedziorski, R., Choi, Y., & Hajishirzi, H. (2019) · North American Chapter of the Association for Computational Linguistics · doi:10.18653/v1/n19-1245 · Cited by 956 · see citing works

Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference

McCoy, R. T., Pavlick, E., & Linzen, T. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1334 · Cited by 944 · see citing works

The Winograd Schema Challenge

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HellaSwag: Can a Machine Really Finish Your Sentence?

Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., & Choi, Y. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1472 · Cited by 830 · see citing works

WikiQA: A Challenge Dataset for Open-Domain Question Answering

Yang, Y., Yih, W.-T., & Meek, C. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1237 · Cited by 829 · see citing works

GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask NetworksPreprint

Chen, Z., Badrinarayanan, V., Lee, C., & Rabinovich, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.02257 · Cited by 797 · see citing works

Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scalesPreprint

Pang, B., & Lee, L. (2005) · arXiv (Cornell University) · doi:10.48550/arxiv.cs/0506075 · Cited by 744 · see citing works

SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference

Zellers, R., Bisk, Y., Schwartz, R., & Choi, Y. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1009 · Cited by 712 · see citing works

SQuAD: 100,000+ Questions for Machine Comprehension of Text

Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016) · arXiv · doi:10.48550/arxiv.1606.05250 · Cited by 678 · see citing works

A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents

Cohan, A., Dernoncourt, F., Kim, D. S., Bui, T., Kim, S., Chang, W., & Goharian, N. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) · doi:10.18653/v1/n18-2097 · Cited by 658 · see citing works

Adversarial NLI: A New Benchmark for Natural Language Understanding

Nie, Y., Williams, A., Dinan, E., Bansal, M., Weston, J., & Kiela, D. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.441 · Cited by 656 · see citing works

ERASER: A Benchmark to Evaluate Rationalized NLP Models

DeYoung, J., Jain, S., Rajani, N. F., Lehman, E., Xiong, C., Socher, R., & Wallace, B. C. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.408 · Cited by 561 · see citing works

The Seventh PASCAL Recognizing Textual Entailment Challenge.

Bentivogli, L., Clark, P. E., Dagan, I., & Giampiccolo, D. (2008) · Cited by 533 · see citing works

UNIFIEDQA: Crossing Format Boundaries with a Single QA System

Khashabi, D., Min, S., Khot, T., Sabharwal, A., Tafjord, O., Clark, P., & Hajishirzi, H. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.171 · Cited by 514 · see citing works

The CommitmentBank: Investigating projection in naturally occurring discourse

de Marneffe, M.-C., Simons, M., & Tonhauser, J. (2019) · Digital Access to Libraries · doi:10.18148/sub/2019.v23i2.601 · Cited by 465 · see citing works

Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Clark, P., Cowhey, I., Etzioni, O., Khot, T., Sabharwal, A., Schoenick, C., & Tafjord, O. (2018) · arXiv · doi:10.48550/arxiv.1803.05457 · Cited by 462 · see citing works

Multi-News: A Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model

Fabbri, A., Li, I., She, T., Li, S., & Radev, D. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1102 · Cited by 449 · see citing works

BoolQ: Exploring the Surprising Difficulty of Natural Yes/No QuestionsPreprint

Clark, C., Lee, K., Chang, M.-W., Kwiatkowski, T., Collins, M., & Toutanova, K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.10044 · Cited by 425 · see citing works

SciTaiL: A Textual Entailment Dataset from Science Question Answering

Khot, T., Sabharwal, A., & Clark, P. E. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12022 · Cited by 416 · see citing works

HellaSwag: Can a Machine Really Finish Your Sentence?Preprint

Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., & Choi, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.07830 · Cited by 408 · see citing works

DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs

Dua, D., Wang, Y., Dasigi, P., Stanovsky, G., Singh, S., & Gardner, M. (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/n19-1246 · Cited by 393 · see citing works

BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Clark, C., Lee, K., Chang, M.-W., Kwiatkowski, T., Collins, M., & Toutanova, K. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1300 · Cited by 391 · see citing works

Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences

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Choice of Plausible Alternatives: An Evaluation of Commonsense Causal Reasoning

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CommonsenseQA: A Question Answering Challenge Targeting Commonsense KnowledgePreprint

Talmor, A., Herzig, J., Lourie, N., & Berant, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.00937 · Cited by 296 · see citing works

WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations

Pilehvar, M. T., & Camacho-Collados, J. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1128 · Cited by 296 · see citing works

GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks

Zhao, C., Badrinarayanan, V., Lee, C., & Rabinovich, A. (2018) · International Conference on Machine Learning · Cited by 275 · see citing works

Crowdsourcing Multiple Choice Science Questions

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DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs

Dua, D., Wang, Y., Dasigi, P., Stanovsky, G., Singh, S., & Gardner, M. (2019) · arXiv · doi:10.48550/arxiv.1903.00161 · Cited by 238 · see citing works

BillSum: A Corpus for Automatic Summarization of US Legislation

Kornilova, A., & Eidelman, V. (2019) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d19-5406 · Cited by 232 · see citing works

On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines

Mosbach, M., Andriushchenko, M., & Klakow, D. (2020) · arXiv · doi:10.48550/arxiv.2006.04884 · Cited by 220 · see citing works

ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading ComprehensionPreprint

Zhang, S., Liu, X., Liu, J., Gao, J., Duh, K., & Durme, B. V. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.12885 · Cited by 201 · see citing works

SemEval-2019 Task 4: Hyperpartisan News Detection

Kiesel, J., Mestre, M., Shukla, R., Vincent, E., Adineh, P., Corney, D., Stein, B., & Potthast, M. (2019) · Proceedings of the 13th International Workshop on Semantic Evaluation · doi:10.18653/v1/s19-2145 · Cited by 193 · see citing works

Toward semantics-based answer pinpointing

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BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization

Sharma, E., Li, C., & Wang, L. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1212 · Cited by 173 · see citing works

Better Fine-Tuning by Reducing Representational CollapsePreprint

Aghajanyan, A., Shrivastava, A., Gupta, A., Goyal, N., Zettlemoyer, L., & Gupta, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.03156 · Cited by 144 · see citing works

Adversarial Training for Large Neural Language ModelsPreprint

Liu, X., Cheng, H., He, P., Chen, W., Wang, Y., Poon, H., & Gao, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.08994 · Cited by 143 · see citing works

MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based FormalismsPreprint

Amini, A., Gabriel, S., Lin, P., Koncel-Kedziorski, R., Choi, Y., & Hajishirzi, H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.13319 · Cited by 137 · see citing works

Cosmos QA: Machine Reading Comprehension with Contextual Commonsense ReasoningPreprint

Huang, L., Bras, R. J. L., Bhagavatula, C., & Choi, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.00277 · Cited by 113 · see citing works

The use of external knowledge in factoid QA

Hovy, E. H., Hermjakob, U., & Lin, C.-Y. (2001) · Cited by 112 · see citing works

WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning RepresentationsPreprint

Pilehvar, M. T., & Camacho-Collados, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.09121 · Cited by 87 · see citing works

InfoBERT: Improving Robustness of Language Models from An Information Theoretic PerspectivePreprint

Wang, B., Wang, S., Cheng, Y., Gan, Z., Jia, R., Li, B., & Liu, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.02329 · Cited by 78 · see citing works

This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation

Zhang, R., & Tetreault, J. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1043 · Cited by 55 · see citing works

Long Document Classification From Local Word Glimpses via Recurrent Attention Learning

He, J., Wang, L., Liu, L., Feng, J., & Wu, H. (2019) · IEEE Access · doi:10.1109/access.2019.2907992 · Cited by 31 · see citing works

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

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805

Finetuned Language Models Are Zero-Shot Learners

Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., & Le, Q. V. (2021) · arXiv · doi:10.48550/arxiv.2109.01652 · Cited by 1,101 · see citing works

Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

Min, S., Lyu, X., Holtzman, A., Artetxe, M., Lewis, M., Hajishirzi, H., & Zettlemoyer, L. (2022) · arXiv · doi:10.48550/arxiv.2202.12837 · Cited by 752 · see citing works

Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

Min, S., Lyu, X., Holtzman, A., Artetxe, M., Lewis, M., Hajishirzi, H., & Zettlemoyer, L. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.759 · Cited by 755 · see citing works

Cross-Task Generalization via Natural Language Crowdsourcing Instructions

Mishra, S., Khashabi, D., Baral, C., & Hajishirzi, H. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.244 · Cited by 269 · see citing works

UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models

Xie, T., Wu, C. H., Shi, P., Zhong, R., Scholak, T., Yasunaga, M., Wu, C.-S., Zhong, M., Yin, P., Wang, S. I., et al. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.39 · Cited by 271 · see citing works

Parameter-efficient fine-tuning in large language models: a survey of methodologies

Wang, L., Chen, S., Jiang, L., Pan, S., Cai, R., Yang, S., & Yang, F. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11236-4 · Cited by 186 · see citing works

MetaICL: Learning to Learn In Context

Min, S., Lewis, M., Zettlemoyer, L., & Hajishirzi, H. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2022.naacl-main.201 · Cited by 169 · see citing works

Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System

Su, Y., Shu, L., Mansimov, E., Gupta, A., Cai, D., Lai, Y.-A., & Zhang, Y. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.319 · Cited by 152 · see citing works

Zhongjing: Enhancing the Chinese Medical Capabilities of Large Language Model through Expert Feedback and Real-World Multi-Turn Dialogue

Yang, S., Zhao, H., Zhu, S., Zhou, G., Hongfei, X., Jia, Y., & Zan, H. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29907 · Cited by 145 · see citing works

NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks

Mishra, S. R., Mitra, A., Varshney, N., Sachdeva, B., Clark, P. A., Baral, C. R., & Kalyan, A. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.246 · Cited by 122 · see citing works

CPT: a pre-trained unbalanced transformer for both Chinese language understanding and generation

Shao, Y., Geng, Z., Liu, Y., Dai, J., Yan, H., Yang, F., Li, Z., Bao, H., & Qiu, X. (2024) · Science China Information Sciences · doi:10.1007/s11432-021-3536-5 · Cited by 155 · see citing works

Efficient Methods for Natural Language Processing: A Survey

Treviso, M., Lee, J.-U., Ji, T., van Aken, B., Cao, Q., Ciosici, M. R., Hassid, M., Heafield, K., Hooker, S., Raffel, C., et al. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00577 · Cited by 124 · see citing works

Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections

Zhong, R., Lee, K., Zhang, Z., & Klein, D. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.244 · Cited by 118 · see citing works

UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language Modeling

Yang, Z., Gan, Z., Wang, J., Hu, X., Ahmed, F., Liu, Z., Lu, Y., & Wang, L. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20059-5_30 · Cited by 83 · see citing works

A Survey of Multi-task Learning in Natural Language Processing: Regarding Task Relatedness and Training Methods

Zhang, Z., Yu, W., Yu, M., Guo, Z., & Jiang, M. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.66 · Cited by 79 · see citing works

SciRepEval: A Multi-Format Benchmark for Scientific Document Representations

Singh, A., D’Arcy, M., Cohan, A., Downey, D., & Feldman, S. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.338 · Cited by 95 · see citing works

ExT5: Towards Extreme Multi-Task Scaling for Transfer LearningPreprint

Aribandi, V., Tay, Y., Schuster, T., Rao, J., Zheng, H., Mehta, S. V., Zhuang, H., Tran, V. Q., Bahri, D., Ni, J., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.10952 · Cited by 94 · see citing works

Meta-learning via Language Model In-context Tuning

Chen, Y., Zhong, R., Zha, S., Karypis, G., & He, H. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.53 · Cited by 84 · see citing works

ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts

Asai, A., Salehi, M., Peters, M. E., & Hajishirzi, H. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.446 · Cited by 78 · see citing works

Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems

FitzGerald, J., Ananthakrishnan, S., Arkoudas, K., Bernardi, D., Bhagia, A., Delli Bovi, C., Cao, J., Chada, R., Chauhan, A., Chen, L., et al. (2022) · Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3534678.3539173 · Cited by 68 · see citing works

BiasFinder: Metamorphic Test Generation to Uncover Bias for Sentiment Analysis Systems

Asyrofi, M. H., Yang, Z., Yusuf, I. N. B., Kang, H. J., Thung, F., & Lo, D. (2021) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2021.3136169 · Cited by 69 · see citing works

A Comprehensive Survey of Abstractive Text Summarization Based on Deep Learning

Zhang, M., Zhou, G., Yu, W., Huang, N., & Liu, W. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/7132226 · Cited by 67 · see citing works

Deep Transfer Learning & Beyond: Transformer Language Models in Information Systems Research

Gruetzemacher, R., & Paradice, D. (2022) · ACM Computing Surveys · doi:10.1145/3505245 · Cited by 61 · see citing works

Zero-Shot Text Classification with Self-Training

Gera, A., Halfon, A., Shnarch, E., Perlitz, Y., Ein‐Dor, L., & Slonim, N. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.73 · Cited by 57 · see citing works

MultiInstruct: Improving Multi-Modal Zero-Shot Learning via Instruction Tuning

Xu, Z., Shen, Y., & Huang, L. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.641 · Cited by 57 · see citing works

The foundational capabilities of large language models in predicting postoperative risks using clinical notes

Alba, C., Xue, B., Abraham, J., Kannampallil, T., & Lu, C. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01489-2 · Cited by 46 · see citing works

Usage frequency and application variety of research methods in library and information science: Continuous investigation from 1991 to 2021

Zhang, C., Tian, L., & Chu, H. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103507 · Cited by 42 · see citing works

Domain-matched Pre-training Tasks for Dense Retrieval

Oğuz, B., Lakhotia, K., Gupta, A., Lewis, P., Karpukhin, V., Piktus, A., Chen, X., Riedel, S., Yih, S., Gupta, S., et al. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.114 · Cited by 46 · see citing works

A taxonomy and review of generalization research in NLPPreprint

Hupkes, D., Giulianelli, M., Dankers, V., Artetxe, M., Elazar, Y., Pimentel, T., Christodoulopoulos, C., Lasri, K., Saphra, N., Sinclair, A., et al. (2022) · arXiv (Cornell University) · doi:10.3929/ethz-b-000638992 · Cited by 41 · see citing works

Do LLMs Understand Social Knowledge? Evaluating the Sociability of Large Language Models with SocKET Benchmark

Choi, M., Pei, J., Kumar, S., Shu, C., & Jurgens, D. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.699 · Cited by 38 · see citing works

Bridging Remote Sensors with Multisensor Geospatial Foundation Models

Han, B., Zhang, S., Shi, X., & Reichstein, M. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.02631 · Cited by 37 · see citing works

DesPrompt: Personality-descriptive prompt tuning for few-shot personality recognition

Wen, Z., Cao, J., Yang, Y., Wang, H., Yang, R., & Liu, S. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103422 · Cited by 30 · see citing works

PromptST: Prompt-Enhanced Spatio-Temporal Multi-Attribute Prediction

Zhang, Z., Zhao, X., Liu, Q., Zhang, C., Ma, Q., Wang, W., Zhao, H., Wang, Y., & Liu, Z. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3615016 · Cited by 29 · see citing works

Qibo: A Large Language Model for traditional Chinese medicine

Jia, Y., Ji, X., Wang, X., Zhang, H., Meng, Z., Zhang, J., Chen, Z., Zhuang, P., Xu, D., Guo, W., et al. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127672 · Cited by 60 · see citing works

Assemble Foundation Models for Automatic Code Summarization

Gu, J., Salza, P., & Gall, H. C. (2022) · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner53432.2022.00112 · Cited by 30 · see citing works

Joint reasoning with knowledge subgraphs for Multiple Choice Question Answering

Zhang, Q., Chen, S., Fang, M., & Chen, X. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103297 · Cited by 26 · see citing works

A survey of text classification based on pre-trained language model

Wu, Y., & Wan, J. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128921 · Cited by 35 · see citing works

Summarization, simplification, and generation: The case of patents

Casola, S., & Lavelli, A. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117627 · Cited by 26 · see citing works

Improving In-Context Few-Shot Learning via Self-Supervised Training

Chen, M., Du, J., Pasunuru, R., Mihaylov, T., Iyer, S. V., Stoyanov, V., & Kozareva, Z. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.260 · Cited by 28 · see citing works

Transcending Scaling Laws with 0.1% Extra Compute

Tay, Y., Wei, J., Chung, H. W., Tran, V., So, D. R., Shakeri, S., Garcia, X., Zheng, S., Rao, J., Chowdhery, A., et al. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.91 · Cited by 41 · see citing works

STraTA: Self-Training with Task Augmentation for Better Few-shot Learning

Vu, T., Luong, M.-T., Le, Q. V., Simon, G., & Iyyer, M. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.462 · Cited by 30 · see citing works

MVP: Multi-task Supervised Pre-training for Natural Language Generation

Tang, T., Li, J., Zhao, W. X., & Wen, J.-R. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.558 · Cited by 23 · see citing works

Question Answering Infused Pre-training of General-Purpose Contextualized Representations

Jia, R., Lewis, M., & Zettlemoyer, L. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.59 · Cited by 23 · see citing works

A Thorough Evaluation of Task-Specific Pretraining for Summarization

Rothe, S., Maynez, J., & Narayan, S. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.12 · Cited by 24 · see citing works

In-BoXBART: Get Instructions into Biomedical Multi-Task Learning

Parmar, M., Mishra, S. R., Purohit, M., Luo, M., Mohammad, M., & Baral, C. R. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.10 · Cited by 28 · see citing works

Challenges and Opportunities of Using Transformer-Based Multi-Task Learning in NLP Through ML Lifecycle: A Position Paper

Torbarina, L., Ferkovic, T., Roguski, Ł., Mihelčić, V., Šarlija, B., & Kraljević, Ž. (2024) · Natural Language Processing Journal · doi:10.1016/j.nlp.2024.100076 · Cited by 29 · see citing works

Tc-llama 2: fine-tuning LLM for technology and commercialization applications

Yeom, J., Lee, H., Byun, H., Kim, Y., Byun, J., Choi, Y., Kim, S., & Song, K. (2024) · Journal Of Big Data · doi:10.1186/s40537-024-00963-0 · Cited by 20 · see citing works

IndicXNLI: Evaluating Multilingual Inference for Indian Languages

Aggarwal, D., Gupta, V., & Kunchukuttan, A. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.755 · Cited by 25 · see citing works

Exploring the Role of Task Transferability in Large-Scale Multi-Task Learning

Padmakumar, V., Lausen, L., Ballesteros, M., Zha, S., He, H., & Karypis, G. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.183 · Cited by 17 · see citing works

Materials Informatics: Emergence to Autonomous Discovery in the Age of AI

Lookman, T., Liu, Y., & Gao, Z. (2026) · Advanced Materials · doi:10.1002/adma.202515941 · Cited by 18 · see citing works

Eye Gaze and Self-attention: How Humans and Transformers Attend Words in Sentences

Bensemann, J., Peng, A. Y., Benavides‐Prado, D., Chen, Y., Tan, N. Ö., Corballis, P. M., Riddle, P., & Witbrock, M. (2022) · Workshop on Cognitive Modeling and Computational Linguistics (CMCL) · doi:10.18653/v1/2022.cmcl-1.9 · Cited by 17 · see citing works

Human-in-the-loop and large language models in smart manufacturing: Current applications, challenges, and perspectives

Bajestani, M. S., Mun, D., & Kim, D. B. (2026) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2026.04.027 · Cited by 18 · see citing works

Learning Non-Autoregressive Models from Search for Unsupervised Sentence Summarization

Liu, P., Huang, C., & Mou, L. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.545 · Cited by 15 · see citing works

Meta-Learning the Difference: Preparing Large Language Models for Efficient Adaptation

Hou, Z., Salazar, J., & Polovets, G. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00517 · Cited by 17 · see citing works

Efficient Multi-Task Auxiliary Learning: Selecting Auxiliary Data by Feature Similarity

Kung, P.-N., Yin, S.-S., Chen, Y., Yang, T.-H., & Chen, Y.-N. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.34 · Cited by 15 · see citing works

ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning

Don-Yehiya, S., Venezian, E., Raffel, C., Slonim, N., & Choshen, L. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.46 · Cited by 18 · see citing works

P3 Ranker: Mitigating the Gaps between Pre-training and Ranking Fine-tuning with Prompt-based Learning and Pre-finetuning

Hu, X., Yu, S., Xiong, C., Liu, Z., Liu, Z., & Yu, G. (2022) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3477495.3531786 · Cited by 14 · see citing works

CCQA: A New Web-Scale Question Answering Dataset for Model Pre-Training

Huber, P., Aghajanyan, A., Oğuz, B., Okhonko, D., Yih, S., Gupta, S., & Chen, X. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.184 · Cited by 14 · see citing works

Geographic Adaptation of Pretrained Language Models

Hofmann, V., Glavaš, G., Ljubešić, N., Pierrehumbert, J. B., & Schütze, H. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00652 · Cited by 18 · see citing works

A Comprehensive Survey on Summarization Techniques

Uppalapati, P. J., Dabbiru, M., & Rao, K. V. (2023) · SN Computer Science · doi:10.1007/s42979-023-02007-5 · Cited by 13 · see citing works

Single-Document Abstractive Text Summarization: A Systematic Literature Review

Rao, A. B., Aithal, S. G., & Singh, S. (2024) · ACM Computing Surveys · doi:10.1145/3700639 · Cited by 16 · see citing works

Probing BERT for Ranking Abilities

Wallat, J., Beringer, F., Anand, A., & Anand, A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-28238-6_17 · Cited by 14 · see citing works

Reducing the Cost: Cross-Prompt Pre-finetuning for Short Answer Scoring

Funayama, H., Asazuma, Y., Matsubayashi, Y., Mizumoto, T., & Inui, K. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-36272-9_7 · Cited by 13 · see citing works

Cross-lingual Intermediate Fine-tuning improves Dialogue State Tracking

Moghe, N., Steedman, M. J., & Birch, A. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.87 · Cited by 13 · see citing works

Few-shot fine-tuning SOTA summarization models for medical dialogues

Navarro, D. F., Dras, M., & Berkovsky, S. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop (NAACL SRW) · doi:10.18653/v1/2022.naacl-srw.32 · Cited by 12 · see citing works

MIGA: A Unified Multi-Task Generation Framework for Conversational Text-to-SQL

Fu, Y., Ou, W., Zhou, Y., & Lin, Y. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i11.26504 · Cited by 12 · see citing works

Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective

Yang, P., Wang, J., Gan, R., Zhu, X., Zhang, L., Wu, Z., Gao, X., Zhang, J., & Sakai, T. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.474 · Cited by 12 · see citing works

Soft Prompt Decoding for Multilingual Dense Retrieval

Huang, Z., Zeng, H., Zamani, H., & Allan, J. (2023) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3539618.3591769 · Cited by 13 · see citing works

CrossCodeBench: Benchmarking Cross-Task Generalization of Source Code Models

Niu, C., Li, C., Ng, V., & Luo, B. (2023) · 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) · doi:10.1109/icse48619.2023.00055 · Cited by 11 · see citing works

Task Compass: Scaling Multi-task Pre-training with Task Prefix

Zhang, Z., Wang, S., Yi‐chong, X., Fang, Y., Yu, W., Liu, Y., Zhao, H. Y., Zhu, C., & Zeng, M. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.416 · Cited by 12 · see citing works

Where to start? Analyzing the potential value of intermediate models

Choshen, L., Venezian, E., Don-Yehiya, S., Slonim, N., & Katz, Y. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.90 · Cited by 12 · see citing works

Exploring Universal Intrinsic Task Subspace for Few-Shot Learning via Prompt Tuning

Qin, Y., Wang, X., Su, Y., Lin, Y., Ding, N., Yi, J., Chen, W., Liu, Z., Li, J., Hou, L., et al. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3430545 · Cited by 44 · see citing works

Development and Evaluation of a Digital Scribe: Conversation Summarization Pipeline for Emergency Department Counseling Sessions towards Reducing Documentation BurdenPreprint

Sezgın, E., Sirrianni, J., & Kranz, K. (2023) · medRxiv · doi:10.1101/2023.12.06.23299573 · Cited by 10 · see citing works

Conversational Text-to-SQL: An Odyssey into State-of-the-Art and Challenges Ahead

Parthasarathi, S. H. K., Zeng, L., & Hakkani-Tur, D. Z. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096170 · Cited by 9 · see citing works

Domain-Adapted BERT-based Models for Nuanced Arabic Dialect Identification and Tweet Sentiment Analysis

Bayrak, G., & Issifu, A. M. (2022) · Arabic Natural Language Processing Workshop (WANLP) · doi:10.18653/v1/2022.wanlp-1.43 · Cited by 9 · see citing works

Zero-Shot Learners for Natural Language Understanding via a Unified Multiple-Choice Perspective

Wang, J., Yang, P., Gan, R., Zhang, Y., Zhang, J., & Sakai, T. (2023) · IEEE Access · doi:10.1109/access.2023.3343123 · Cited by 9 · see citing works

Design of superalloys with multiple properties via multi-task learning

Wang, W., Jiang, X. F., Li, W., Zhang, C., Liu, P., Tian, S., Lookman, T., & Su, Y. (2025) · Acta Materialia · doi:10.1016/j.actamat.2025.121161 · Cited by 8 · see citing works

Leveraging QA Datasets to Improve Generative Data Augmentation

Mekala, D., Vu, T., Schick, T., & Shang, J. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.660 · Cited by 8 · see citing works

Multi-task Learning for Features Extraction in Financial Annual Reports

Montariol, S., Martinc, M., Pelicon, A., Pollak, S., Koloski, B., Lončarski, I., Valentinčič, A., Šuštar, K. S., Ichev, R., & Žnidaršič, M. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-23633-4_1 · Cited by 8 · see citing works

Eliciting and Understanding Cross-task Skills with Task-level Mixture-of-Experts

Ye, Q., Zha, J., & Ren, X. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.189 · Cited by 11 · see citing works

CLUES: A Benchmark for Learning Classifiers using Natural Language Explanations

Menon, R. R., Ghosh, S., & Srivastava, S. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.451 · Cited by 9 · see citing works

Analyzing Multi-Task Learning for Abstractive Text Summarization

Kirstein, F., Wahle, J. P., Ruas, T., & Gipp, B. (2022) · Workshop on Generation, Evaluation and Metrics (GEM) · doi:10.18653/v1/2022.gem-1.5 · Cited by 7 · see citing works

Uncovering Inconsistencies and Contradictions in Financial Reports using Large Language Models

Deußer, T., Leonhard, D., Hillebrand, L., Berger, A., Khaled, M. B., Heiden, S., Dilmaghani, T., Kliem, B., Loitz, R., Bauckhage, C., et al. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386673 · Cited by 7 · see citing works

SheffieldVeraAI at SemEval-2023 Task 3: Mono and Multilingual Approaches for News Genre, Topic and Persuasion Technique Classification

Wu, B., Razuvayevskaya, O., Heppell, F., Leite, J. A., Scarton, C., Bontcheva, K., & Song, X. (2023) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/2023.semeval-1.275 · Cited by 9 · see citing works

Exploring Mode Connectivity for Pre-trained Language Models

Qin, Y., Qian, C., Yi, J., Chen, W., Lin, Y., Xu, H., Liu, Z., Sun, M., & Zhou, J. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.451 · Cited by 7 · see citing works

Decrypting Cryptic Crosswords: Semantically Complex Wordplay Puzzles as a Target for NLPPreprint

Rozner, J., Potts, C., & Mahowald, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.08620 · Cited by 7 · see citing works

Prototype-based HyperAdapter for Sample-Efficient Multi-task Tuning

Zhao, H., Fu, J., & He, Z. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.280 · Cited by 6 · see citing works

Enhancing Low-resource Fine-grained Named Entity Recognition by Leveraging Coarse-grained Datasets

Lee, S. J., Oh, S., & Jung, W. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.197 · Cited by 6 · see citing works

A Knowledge-enhanced Two-stage Generative Framework for Medical Dialogue Information Extraction

Hu, Z., Ni, Z., Shi, J. Y., Xu, S., & Xu, B. (2024) · Machine Intelligence Research · doi:10.1007/s11633-023-1461-5 · Cited by 6 · see citing works

Do the Findings of Document and Passage Retrieval Generalize to the Retrieval of Responses for Dialogues?

Penha, G., & Hauff, C. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-28241-6_9 · Cited by 6 · see citing works

Multitask Pretraining with Structured Knowledge for Text-to-SQL Generation

Giaquinto, R. A., Zhang, D., Kleiner, B., Li, Y., Tan, M., Bhatia, P., Nallapati, R. M., & Ma, X. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.620 · Cited by 6 · see citing works

Hexatagging: Projective Dependency Parsing as Tagging

Amini, A., Liu, T., & Cotterell, R. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-short.124 · Cited by 6 · see citing works

Multi Self-Supervised Pre-Finetuned Transformer Fusion for Better Vehicle Detection

Zheng, J., & Ren, J. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3374759 · Cited by 6 · see citing works

DaCy: A Unified Framework for Danish NLPPreprint

Enevoldsen, K., Hansen, L., & Nielbo, K. L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.05295 · Cited by 8 · see citing works

ParaNet:Parallel Networks with Pre-trained Models for Text Classification

Wu, Y., Guo, X. Y., Wei, Y., & Chen, X. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-46671-7_9 · Cited by 5 · see citing works

Improving Numeracy by Input Reframing and Quantitative Pre-Finetuning Task

Chen, C.-C., Takamura, H., Kobayashi, I., & Miyao, Y. (2023) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2023.findings-eacl.4 · Cited by 5 · see citing works

DFM: Dialogue foundation model for universal large-scale dialogue-oriented task learning

Chen, Z., Ma, D., Li, H., Chen, L., Ji, J., Liu, Y., Chen, B., Wu, M., Zhu, S., Dong, X., et al. (2025) · AI Open · doi:10.1016/j.aiopen.2025.04.001 · Cited by 10 · see citing works

Locally Aggregated Feature Attribution on Natural Language Model Understanding

Zhang, S., Wang, J., Jiang, H., & Song, R. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.159 · Cited by 5 · see citing works

Pre-Finetuning for Few-Shot Emotional Speech Recognition

Chen, M., & Zhou, Y. (2023) · Interspeech (USB) · doi:10.21437/interspeech.2023-136 · Cited by 6 · see citing works

Learning to Generalize Compositionally by Transferring Across Semantic Parsing TasksPreprint

Wang, Z., Shaw, P. M., Linzen, T., & Sha, F. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.05013 · Cited by 8 · see citing works

To Pretrain or Not to Pretrain: Examining the Benefits of Pretrainng on Resource Rich Tasks

Wang, S., Khabsa, M., & Ma, H. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.200 · Cited by 23 · see citing works

Amuro & Char: Analyzing the Relationship between Pre-Training and Fine-Tuning of Large Language Models

Sun, K., & Dredze, M. H. (2025) · Workshop on Representation Learning for NLP (RepL4NLP) · doi:10.18653/v1/2025.repl4nlp-1.11 · Cited by 3 · see citing works

Pre-trained Models for Representation Learning

Lin, Y., Ding, N., Liu, Z., & Sun, M. (2023) · Springer eBooks · doi:10.1007/978-981-99-1600-9_5

To Pretrain or Not to Pretrain: Examining the Benefits of Pretraining on Resource Rich TasksPreprint

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