Designing and Interpreting Probes with Control Tasks

Adam: A Method for Stochastic Optimization

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

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 · Cited by 73,173

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 54,325

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

Untitled work

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Distributed Representations of Words and Phrases and their Compositionality

Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533

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

Building a Large Annotated Corpus of English: The Penn Treebank

Marcus, M. (1993) · doi:10.21236/ada273556 · Cited by 7,405

Understanding deep learning requires rethinking generalization

Zhang, C., Bengio, S., Hardt, M., Recht, B., & Vinyals, O. (2016) · arXiv · doi:10.48550/arxiv.1611.03530 · Cited by 2,838

Generating Typed Dependency Parses from Phrase Structure Parses

de Marneffe, M.-C., MacCartney, B., & Manning, C. D. (2006) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/3wtkaidk6ea8 · Cited by 2,289

Low-Complexity Probing via Finding Subnetworks

Cao, S., Sanh, V., & Rush, A. M. (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.74 · Cited by 51

Choose Your Lenses: Flaws in Gender Bias Evaluation

Orgad, H., & Belinkov, Y. (2022) · Workshop on Gender Bias in Natural Language Processing (GeBNLP) · doi:10.18653/v1/2022.gebnlp-1.17 · Cited by 24

The Low-Dimensional Linear Geometry of Contextualized Word Representations

Hernandez, E. J., & Andreas, J. (2021) · Conference on Computational Natural Language Learning · doi:10.18653/v1/2021.conll-1.7 · Cited by 32

A disentangled linguistic graph model for explainable aspect-based sentiment analysis

Mei, X., Zhou, Y., Zhu, C., Wu, M., Li, M., & Pan, S. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.110150 · Cited by 19

On the Interplay Between Fine-tuning and Sentence-Level Probing for Linguistic Knowledge in Pre-Trained Transformers

Mosbach, M., Khokhlova, A., Hedderich, M. A., & Klakow, D. (2020) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2020.blackboxnlp-1.7 · Cited by 21

Generalising to German Plural Noun Classes, from the Perspective of a Recurrent Neural Network

Dankers, V., Langedijk, A., McCurdy, K., Williams, A., & Hupkes, D. (2021) · Conference on Computational Natural Language Learning · doi:10.18653/v1/2021.conll-1.8 · Cited by 18

Probing for Predicate Argument Structures in Pretrained Language Models

Conia, S., & Roberto Navigli, A. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.316 · Cited by 20

Evaluating Document Coherence Modeling

Shen, A., Mistica, M., Salehi, B., Li, H., Baldwin, T., & Qi, J. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00388 · Cited by 22

Probing for Constituency Structure in Neural Language Models

Arps, D., Samih, Y., Kallmeyer, L., & Sajjad, H. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.502 · Cited by 28

Do PLMs Know and Understand Ontological Knowledge?

Wu, W., Jiang, C., Jiang, Y., Xie, P., & Tu, K. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.173 · Cited by 24

Probing Contextual Language Models for Common Ground with Visual Representations

Ilharco, G., Zellers, R., Farhadi, A., & Hajishirzi, H. (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.422 · Cited by 30

A Linguistic Study on Relevance Modeling in Information Retrieval

Fan, Y., Guo, J., Ma, X., Zhang, R., Lan, Y., & Cheng, X. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3450009 · Cited by 21

Chess as a Testbed for Language Model State Tracking

Toshniwal, S., Wiseman, S., Livescu, K., & Gimpel, K. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i10.21390 · Cited by 42

On the Pitfalls of Analyzing Individual Neurons in Language ModelsPreprint

Antverg, O., & Belinkov, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.07483 · Cited by 40

What if This Modified That? Syntactic Interventions with Counterfactual Embeddings

Tucker, M., Qian, P., & Levy, R. (2021) · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 · doi:10.18653/v1/2021.findings-acl.76 · Cited by 16

What do tokens know about their characters and how do they know it?

Kaushal, A., & Mahowald, K. (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.179 · Cited by 16

COMPS: Conceptual Minimal Pair Sentences for testing Robust Property Knowledge and its Inheritance in Pre-trained Language Models

Misra, K., Rayz, J. T., & Ettinger, A. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.213 · Cited by 32

On the Interplay Between Fine-tuning and Sentence-level Probing for Linguistic Knowledge in Pre-trained Transformers

Mosbach, M., Khokhlova, A., Hedderich, M. A., & Klakow, D. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.227 · Cited by 20

When Do You Need Billions of Words of Pretraining Data?Preprint

Zhang, Y., Warstadt, A., Li, H.-S., & Bowman, S. R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.04946 · Cited by 16

Let's Play Mono-Poly: BERT Can Reveal Words' Polysemy Level and Partitionability into SensesPreprint

Soler, A. G., & Apidianaki, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.14694 · Cited by 16

A Bayesian Framework for Information-Theoretic Probing

Pimentel, T., & Cotterell, R. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.229 · Cited by 16

How Gender Debiasing Affects Internal Model Representations, and Why It Matters

Orgad, H., Goldfarb-Tarrant, S., & Belinkov, Y. (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.188 · Cited by 27

Can Vision-Language Models be a Good Guesser? Exploring VLMs for Times and Location Reasoning

Zhang, G., Zhang, Y., Zhang, K., & Volker Tresp, C. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00069 · Cited by 17

Exploring the Role of BERT Token Representations to Explain Sentence Probing Results

Mohebbi, H., Modarressi, A., & Pilehvar, M. T. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.61 · Cited by 28

How Reliable are Model Diagnostics?

Aribandi, V., Tay, Y., & Metzler, D. (2021) · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 · doi:10.18653/v1/2021.findings-acl.155 · Cited by 15

A Non-Linear Structural Probe

White, J. C., Pimentel, T., Saphra, N., & Cotterell, R. (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.12 · Cited by 17

What do end-to-end speech models learn about speaker, language and channel information? A layer-wise and neuron-level analysis

Chowdhury, S. A., Durrani, N., & Ali, A. M. A. (2023) · Computer Speech & Language · doi:10.1016/j.csl.2023.101539 · Cited by 16

Is My Model Using the Right Evidence? Systematic Probes for Examining Evidence-Based Tabular Reasoning

Gupta, V., Bhat, R. A., Ghosal, A., Shrivastava, M., Singh, M., & Srikumar, V. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00482 · Cited by 16

Do Language Models Know the Way to Rome?

Liétard, B., Abdou, M., & Søgaard, A. (2021) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2021.blackboxnlp-1.40 · Cited by 23

Analyzing BERT’s Knowledge of Hypernymy via Prompting

Hanna, M., & Mareček, D. (2021) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2021.blackboxnlp-1.20 · Cited by 18

Evaluating the construct validity of text embeddings with application to survey questions

Fang, Q., Nguyen, D., & Oberski, D. L. (2022) · EPJ Data Science · doi:10.1140/epjds/s13688-022-00353-7 · Cited by 19

How does the pre-training objective affect what large language models learn about linguistic properties?

Alajrami, A., & Αλέτρας, Ν. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-short.16 · Cited by 23

The Curious Case of Hallucinatory (Un)answerability: Finding Truths in the Hidden States of Over-Confident Large Language Models

Slobodkin, A., Goldman, O., Caciularu, A., Dagan, I., & Ravfogel, S. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.220 · Cited by 47

An information theoretic view on selecting linguistic probes

Zhu, Z., & Rudzicz, F. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.744 · Cited by 20

Linguistic Dependencies and Statistical Dependence

Hoover, J. L., Du, W., Sordoni, A., & O’Donnell, T. J. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.234 · Cited by 15

Debiasing Methods in Natural Language Understanding Make Bias More Accessible

Mendelson, M., & Belinkov, Y. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.116 · Cited by 16

Schrödinger's tree—On syntax and neural language models

Kulmizev, A., & Nivre, J. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.796788 · Cited by 20

Connecting Attributions and QA Model Behavior on Realistic Counterfactuals

Ye, X., Nair, R., & Durrett, G. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.447 · Cited by 15

Global Explainability of BERT-Based Evaluation Metrics by Disentangling along Linguistic Factors

Kaster, M., Zhao, W., & Eger, S. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.701 · Cited by 23

Morphosyntactic probing of multilingual BERT models

Ács, J., Hamerlik, E., Schwartz, R., Smith, N. A., & Kornai, A. (2023) · Natural Language Engineering · doi:10.1017/s1351324923000190 · Cited by 25

Quantifying the Contextualization of Word Representations with Semantic Class Probing

Zhao, M., Dufter, P., Yaghoobzadeh, Y., & Schütze, H. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.109 · Cited by 14

Heads-up! Unsupervised Constituency Parsing via Self-Attention Heads

Li, B., Kim, T., Amplayo, R. K., & Keller, F. (2020) · Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the International Joint Conference on Natural Language Processing (AACL-IJCNLP) · doi:10.18653/v1/2020.aacl-main.43 · Cited by 14

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 17

Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model BeliefsPreprint

Hase, P., Diab, M., Çelikyılmaz, A., Li, X., Kozareva, Z., Stoyanov, V., Bansal, M., & Iyer, S. V. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.13654 · Cited by 31

Probing as Quantifying Inductive Bias

Immer, A., Hennigen, L. T., Fortuin, V., & Cotterell, R. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.129 · Cited by 14

BERT Knows Punta Cana is not just beautiful, it’s gorgeous: Ranking Scalar Adjectives with Contextualised Representations

Soler, A. G., & Apidianaki, M. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.598 · Cited by 14

BLIND: Bias Removal With No Demographics

Orgad, H., & Belinkov, Y. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.490 · Cited by 24

A multi-facet analysis of BERT-based entity matching models

Paganelli, M., Tiano, D., & Guerra, F. (2023) · The VLDB Journal · doi:10.1007/s00778-023-00824-x · Cited by 15

Context Analysis for Pre-trained Masked Language Models

Lai, Y.-A., Lalwani, G., & Zhang, Y. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.338 · Cited by 11

Testing for Grammatical Category Abstraction in Neural Language Models

Kim, N., & Smolensky, P. (2021) · ScholarWorks@UMassAmherst (University of Massachusetts Amherst) · doi:10.7275/2nb8-ag59 · Cited by 11

Does BERT Understand Idioms? A Probing-Based Empirical Study of BERT Encodings of Idioms

Tan, M., & Jiang, J. (2021) · International conference Recent advances in natural language processing · doi:10.26615/978-954-452-072-4_156 · Cited by 11

Assessing the Capacity of Transformer to Abstract Syntactic Representations: A Contrastive Analysis Based on Long-distance Agreement

Li, B., Wisniewski, G., & Crabbé, B. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00531 · Cited by 12

How to Probe Sentence Embeddings in Low-Resource Languages: On Structural Design Choices for Probing Task Evaluation

Eger, S., Daxenberger, J., & Gurevych, I. (2020) · Conference on Computational Natural Language Learning · doi:10.18653/v1/2020.conll-1.8 · Cited by 12

Visually Analyzing Contextualized Embeddings

Berger, M. (2020) · Visualization · doi:10.1109/vis47514.2020.00062 · Cited by 13

Introducing Orthogonal Constraint in Structural Probes

Limisiewicz, T., & Mareček, D. (2021) · doi:10.18653/v1/2021.acl-long.36 · Cited by 17

Syntactic Perturbations Reveal Representational Correlates of Hierarchical Phrase Structure in Pretrained Language Models

Alleman, M., Mamou, J., Rio, M. A. D., Tang, H., Kim, Y., & Chung, S. (2021) · Workshop on Representation Learning for NLP (RepL4NLP) · doi:10.18653/v1/2021.repl4nlp-1.27 · Cited by 17

Evaluating representations by the complexity of learning low-loss predictorsPreprint

Whitney, W. F., Song, M. J., Brandfonbrener, D., Altosaar, J., & Cho, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.07368 · Cited by 20

Does syntax matter? A strong baseline for Aspect-based Sentiment Analysis with RoBERTaPreprint

Dai, J., Yan, H. R., Sun, T., Liu, P., & Qiu, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.04986 · Cited by 11

METAGENE-1: Metagenomic Foundation Model for Pandemic MonitoringPreprint

Liu, O., Jaghour, S., Hagemann, J., Wang, S., Wiemels, J., Kaufman, J. T., & Neiswanger, W. (2025) · Qeios · doi:10.32388/fmepo7 · Cited by 17

Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretability

Geiger, A., Ibeling, D., Zur, A., Chaudhary, M., Chauhan, S., Huang, J., Arora, A., Wu, Z., Goodman, N., Potts, C., et al. (2023) · arXiv · doi:10.48550/arxiv.2301.04709 · Cited by 11

Investigate the rainfall-runoff relationship and hydrological concepts inside LSTM

Hu, Y., Li, H., Zhang, C., Wang, T., Chu, W. P., & Li, R. (2025) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2025.106527 · Cited by 12

Not All Models Localize Linguistic Knowledge in the Same Place: A Layer-wise Probing on BERToids’ Representations

Fayyaz, M., Aghazadeh, E., Modarressi, A., Mohebbi, H., & Pilehvar, M. T. (2021) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2021.blackboxnlp-1.29 · Cited by 16

Explainable Natural Language Processing

Søgaard, A. (2021) · Synthesis lectures on human language technologies · doi:10.2200/s01118ed1v01y202107hlt051 · Cited by 32

Neural classification maps for distinct word combinations in Broca’s area

Schell, M., Friederici, A. D., & Zaccarella, E. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.930849 · Cited by 11

Cross-Linguistic Syntactic Difference in Multilingual BERT: How Good is It and How Does It Affect Transfer?

Xu, N., Gui, T., Ma, R., Zhang, Q., Ye, J., Zhang, M., & Huang, X. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.552 · Cited by 14

Finding structure during incremental speech comprehension

Lyu, B., Marslen‐Wilson, W. D., Fang, Y., & TYLER, L. K. (2023) · eLife · doi:10.7554/elife.89311 · Cited by 12

FMMformer: Efficient and Flexible Transformer via Decomposed Near-field and Far-field AttentionPreprint

Nguyen, T. M., Suliafu, V., Osher, S., Chen, L., & Bao, W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.02347 · Cited by 12

Dissecting Lottery Ticket Transformers: Structural and Behavioral Study of Sparse Neural Machine Translation

Movva, R., & Zhao, J. (2020) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2020.blackboxnlp-1.19 · Cited by 9

SLING: Sino Linguistic Evaluation of Large Language Models

Song, Y., Krishna, K., Bhatt, R., & Iyyer, M. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.305 · Cited by 13

Exploring Hydrological Variable Interconnections and Enhancing Predictions for Data‐Limited Basins Through Multi‐Task Learning

Ouyang, W., Gu, X., Ye, L., Liu, X., & Zhang, C. (2025) · Water Resources Research · doi:10.1029/2023wr036593 · Cited by 12

Classifier Probes May Just Learn from Linear Context Features

Kunz, J., & Kuhlmann, M. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.450 · Cited by 14

What’s in Your Head? Emergent Behaviour in Multi-Task Transformer Models

Geva, M., Katz, U., Ben-Arie, A., & Berant, J. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.646 · Cited by 9

INSPECT: Intrinsic and Systematic Probing Evaluation for Code Transformers

Karmakar, A., & Robbes, R. (2023) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2023.3341624 · Cited by 9

Probing Linguistic Information for Logical Inference in Pre-trained Language Models

Chen, Z., & Gao, Q. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i10.21294 · Cited by 11

Rationalizing predictions by adversarial information calibration

Sha, L., Camburu, O.-M., & Lukasiewicz, T. (2022) · Artificial Intelligence · doi:10.1016/j.artint.2022.103828 · Cited by 11

Probing Text Models for Common Ground with Visual RepresentationsPreprint

Ilharco, G., Zellers, R., Farhadi, A., & Hajishirzi, H. (2020) · arXiv (Cornell University) · Cited by 10

Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP)

(2022) · doi:10.18653/v1/2022.gebnlp-1 · Cited by 9

Unsupervised Distillation of Syntactic Information from Contextualized Word Representations

Ravfogel, S., Elazar, Y., Goldberger, J., & Goldberg, Y. (2020) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2020.blackboxnlp-1.9 · Cited by 12

Finding Structural Knowledge in Multimodal-BERT

Milewski, V., de Lhoneux, M., & Moens, M. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.388 · Cited by 8

Adversarial Scrubbing of Demographic Information for Text Classification

Chowdhury, S. B. R., Ghosh, S., Li, Y., Oliva, J. B., Srivastava, S., & Chaturvedi, S. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.43 · Cited by 13

Linguistic Frameworks Go Toe-to-Toe at Neuro-Symbolic Language Modeling

Prange, J., Schneider, N., & Kong, L. (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.325 · Cited by 9

DeepLens: Interactive Out-of-distribution Data Detection in NLP Models

Song, D., Wang, Z., Huang, Y., Ma, L., & Zhang, T. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580741 · Cited by 9

Transparency Helps Reveal When Language Models Learn Meaning

Wu, Z., Merrill, W., Peng, H. M., Beltagy, I., & Smith, N. A. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00565 · Cited by 8

Examining the rhetorical capacities of neural language models

Zhu, Z., Pan, C., Abdalla, M., & Rudzicz, F. (2020) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2020.blackboxnlp-1.3 · Cited by 12

Meta-tuning Language Models to Answer Prompts Better.Preprint

Zhong, R., Lee, K., Zhang, Z., & Klein, D. (2021) · arXiv (Cornell University) · Cited by 8

diagNNose: A Library for Neural Activation Analysis

Jumelet, J. (2020) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2020.blackboxnlp-1.32 · Cited by 6

Shaking Syntactic Trees on the Sesame Street: Multilingual Probing with Controllable Perturbations

Taktasheva, E., Mikhailov, V., & Artemova, E. (2021) · Workshop on Multilingual Representation Learning (MRL) · doi:10.18653/v1/2021.mrl-1.17 · Cited by 8

To be or not to be an Integer? Encoding Variables for Mathematical Text

Ferreira, D. M., Thayaparan, M., Valentino, M., Rozanova, J., & Freitas, A. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.76 · Cited by 7

Emergent Structures and Training Dynamics in Large Language Models

Teehan, R., Clinciu, M., Serikov, O., Szczechla, E., Seelam, N., Mirkin, S., & Gokaslan, A. (2022) · doi:10.18653/v1/2022.bigscience-1.11 · Cited by 9

A multilabel approach to morphosyntactic probing

Shapiro, N. T., Paullada, A., & Steinert‐Threlkeld, S. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.382 · Cited by 10

Joint Universal Syntactic and Semantic Parsing

Stengel-Eskin, E., Murray, K., Zhang, S., White, A. S., & Durme, B. V. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00396 · Cited by 7

Probing Word Translations in the Transformer and Trading Decoder for Encoder Layers

Xu, H., van Genabith, J., Liu, Q., & Xiong, D. (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.7 · Cited by 13

Bird’s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach

Hou, Y., & Sachan, M. (2021) · doi:10.18653/v1/2021.acl-long.145 · Cited by 8

LMTurk: Few-Shot Learners as Crowdsourcing Workers in a Language-Model-as-a-Service Framework

Zhao, M., Mi, F., Wang, Y., Li, M., Jiang, X., Liu, Q., & Schuetze, H. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.51 · Cited by 12

Probing artificial neural networks: insights from neurosciencePreprint

Ivanova, A. A., Hewitt, J., & Zaslavsky, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.08197 · Cited by 17

On the influence of discourse connectives on the predictions of humans and language models

Britton, J., Cong, Y., Hsu, Y.-Y., Chersoni, E., & Blache, P. (2024) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2024.1363120 · Cited by 6

On convex decision regions in deep network representations

Tětková, L., Brüsch, T., Dorszewski, T., Mager, F. M., Aagaard, R. Ø., Foldager, J., Alstrøm, T. S., & Hansen, L. K. (2025) · Nature Communications · doi:10.1038/s41467-025-60809-y · Cited by 10

Probing for Incremental Parse States in Autoregressive Language Models

Eisape, T., Gangireddy, V., Lévy, R., & Kim, Y. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.203 · Cited by 11

Understanding Large Language Models : Towards Rigorous and Targeted Interpretability Using Probing Classifiers and Self-Rationalisation

Kunz, J. (2024) · Linköping studies in science and technology. Dissertations · doi:10.3384/9789180754712 · Cited by 7

SynSem-ASTE: An Enhanced Multi-Encoder Network for Aspect Sentiment Triplet Extraction With Syntax and Semantics

Liu, L., Qin, T., Zhou, Y., Wang, C., & Guan, X. (2024) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2024.3397961 · Cited by 8

Improving Syntactic Probing Correctness and Robustness with Control Tasks

Ma, W., Wang, B. Z., Zhang, H., Wang, L., Coto‐Solano, R., Hassanpour, S., & Vosoughi, S. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-short.35

Embodied Knowledge in Natural Language Processing: A Prolog-Based Approach to Selected bAbI Tasks

Cuartero-Sánchez, J. M. (2025) · doi:10.13140/rg.2.2.29837.65763

A study on Inference Control in Natural Language Processing(自然言語処理における推論の制御に関する研究)

宮田, 高. (1996) · doi:10.11501/3139771

The integration of language and content: action research on a theory of task design

Jones, A., & Sin, S. (2007) · Journal of Applied Linguistics · doi:10.1558/japl.v1i1.95 · Cited by 2

Natural Language Interface Put in Perspective: Interaction of Search Method and Task Complexity

Wang, Q., Hu, J., & Nass, C. (2005) · Natural Language Processing and Cognitive Science · doi:10.5220/0002566000030012